{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 5 – Support Vector Machines**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to feature scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first code example in chapter 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
    "    ])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAEWCAYAAAB/grGJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFsdJREFUeJzt3X+MZWd5H/DvgxcX4sXdUNAuyA1bhEmBFKx41KYB4jFKioJEiOCPEhI3kYLWMUIyRbQNyDQLQ6VEIQUSTKyNYI1I1IgSUgEl4EIYHNdWwrq7BGhZh5DabD0L5seYHTv24Nm3f8zant15d3fWszPn3jufjzTaO+eeH4+e+55X3z1z7r3VWgsAAHCyxw1dAAAAjCJBGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgI5t57LyU57ylLZ79+51HfC+++7LRRddtK59TCJ96dOXPn3pO199uf3227/dWnvqeShp7JjnN46+rHb48OEsLS3luc997tCljBzjpW+z5/lzCsq7d+/OgQMHHntVSWZnZzM9Pb2ufUwifenTlz596TtffamqO9dfzXgyz28cfVlteno68/Pz6x5zk8h46dvsed6tFwAA0CEoAwBAh6AMAAAdgjIAAHQIygAA0CEoAwBAh6AMAAAdgjLAmKqq11fVgap6sKpuPOW5H6qq91XVt6vq3qq6eSNr2bUrqVr+ufLK6Uce79q1kUcFtoIh55dz+sIRAEbK3UnekeSlSZ54ynP7sjzHPyfJd5NctpGFfPOb57YcYK2GnF8EZYAx1Vr7aJJU1VSSSx5eXlX/NMnPJbmktfb9E4tv3/wKAcaboAwwef55kjuTvK2qrkoyl2Rva+1PeitX1Z4ke5Jk586dmZ2dfQyHnD7tM49tf5NnYWFBL04xPz+fpaUlfekwXlaaPu0zG90jQRlg8lyS5MeS/EmSpyf5l0n+e1X979ba/zl15dbavizfqpGpqak2PT19Xos53/sbV7Ozs3pxih07dmR+fl5fOoyXtdnoHnkzH8Dk+fskP0jyjtbaYmvt80k+l+RfDVsWwHgRlAEmz193lrWNPODOnee2HGCthpxfBGWAMVVV26rqCUkuSHJBVT2hqrYluTnJXUnefGKdFya5MsmnN6qWo0eT1pZ/Pve52UceHz26UUcEtooh5xdBGWB8XZfl2yx+PckvnXh8XWvtB0lekeRlSe5N8gdJ/k1r7atDFQowjryZD2BMtdb2Jtl7mue+kuU38QHwGLmiDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAY6qqXl9VB6rqwaq6ccXyn6iq/1FV362qe6rqv1bV0wYsFWAsCcoA4+vuJO9I8oFTlv9wkn1Jdid5RpJjSfZvamUAE2Db0AUA8Ni01j6aJFU1leSSFcv/bOV6VfXeJJ/f3OoAxp8rygCT76eSfGXoIgDGjSvKABOsqp6f5D8mecUZ1tmTZE+S7Ny5M7Ozs+s65sLCwrr3MYn0ZbX5+fksLS3pS4fx0rfZfRGUASZUVT0ryZ8luba19henW6+1ti/L9zRnamqqTU9Pr+u4s7OzWe8+JpG+rLZjx47Mz8/rS4fx0rfZfXHrBcAEqqpnJPlMkpnW2oeGrgdgHLmiDDCmqmpblufxC5JcUFVPSPJQkp1J/jzJe1trNwxYIsBYE5QBxtd1SX5jxe+/lORtSVqSZybZW1V7H36ytbZ9U6sDGHOCMsCYaq3tTbL3NE+/bfMqAZhM7lEGAIAOQRkAADoEZQAA6BCUAQCgQ1AGAIAOQRkAADoEZQAA6BCUAQCgQ1AGAIAOQRkAADoEZQAA6BCUAQCgQ1AGAIAOQRkAADoEZQAA6BCUAQCgQ1AGAIAOQRkAADoEZQAA6BCUAQCgQ1AGAIAOQRkAADoEZQAA6BCUAQCgQ1AGAIAOQRlgBFTVTVXVqupVpyyvqrrxxHO/OVR9AFuRoAwwGv5dkuNJZqrqghXL35nkl5Psa639+iCVAWxRgjLACGitfTHJh5I8J8lVSVJVb0nyxiQfTnLNcNWNl127kqrVP7t2DV0ZjA/n0bJtQxcAwCPemuRfJ/mNqtqe5D8l+XSSq1prxwetbIx885vnthxYzXm0zBVlgBHRWvtGkncn2Z3k95LcmuSVrbXFletV1Zur6gtV9f2quqeqPl5VP7b5FQNMNkEZYLTcs+Lxr7bW7u+sM53kfUl+MslLkjyU5DNV9eSNLw9g6xCUAUZEVb0my2/eO3pi0bW99VprL22t7W+tfbm19qUs39P81CQv3JxKAbYGQRlgBFTVy5LcmOTLSZ6f5HCS11bVj65h8ydleT7/3oYVCLAFCcoAA6uqFyX5SJIjSV7aWrsnyXVZfsP1b61hF+9JcijJbRtW5BjZufPclgOrOY+W+dQLgAFV1WVJPpHk3iQ/01qbS5LW2keq6kCSV1TVi1trf3Ga7f9zkhcleVFrbWmz6h5lR4+efR3gzJxHy1xRBhhIVT0ryaeStCxfSf7bU1Z584l/f/s0278ryS8keUlr7esbVijAFuWKMsBAWmtfS3Laj+9vrX0mSfWeq6r3ZPkzl69srX11YyoE2NoEZYAxU1XXZ/mTLn4+yfeq6uGwvdBaWxiuMoDJ4tYLgPHzuix/0sVnk8yt+HnTypWqandVfbKqvldVR6vqvVXlAgnAGm1KUF75feFXXjk96PeFj+J3l88dm8u1h67N0YXh75yfOzaXK268YmRq0Zd+LUP3ZZTOo1GaXzZLa61O87P3lFXfl+RbSZ6W5LIkV2Q5ZAOwBpsSlEfp+8JHqZaHzdw8ky/d+6XMfH5muCJW1HLLXbeMTC360q9l6L6M0nk0SrWMoH+S5MOttQdaa0ez/MbB5w1cE8DYOKc/wR0+fDjT09OP4TCzp33mse1vPWZP+8zm15I8eOGD+auf+Ku0C1pu+MsbcvA9B3Ph4oWbXsfKWo5fcHxkatGXfi3D92X2tM9s9XN6xLw7yaurajbJDyf52SRvHbQigDHiXrWB3bn7zrS0JElLy53PuDOX/s2lalHLyNfCWLg5yZ4k309yQZIPJvlvp65UVXtOrJedO3dmdnZ2XQddWFhY9z4mkb6sNj8/n6WlJX3pMF76Nrsv1Vpb88pTU1PtwIED536Q7ocbLTuHw58Xo1TL3LG5PPN3n5kHHnrgkWVP3PbEfP3ar2fX9s29wVItajkXo3QebUQtVXV7a23qsW09GqrqcUn+Lsm+JO9Msj3JB5Icbq39+9Nt91jn+ZVmZ2ddze/Ql9Wmp6czPz+fQ4cODV3KyDFe+s5XX9Y6z/vUiwHN3DyT4+34ScuW2tIg956qRS1MnCcn+ZEk722tPdha+06S/UleNmxZAONjU4LyKH1f+CjVctuR27K4tHjSssWlxdx65Fa1qGWkaxml82iUahklrbVvZ/mK8jVVta2qdiT55SR/PWxlAONjU+5RXvl94UP/KWGUvrv84NUHH3k8dF9W1jI0fekbpb6M0nk0SvPLCHpllt/Q9x+SLCX58yT/dtCKAMaIN/MBTKjW2qEk00PXATCu3KMMAAAdgjIAAHQIygAA0CEoAwBAh6AMAAAdgjIAAHQIygAA0CEoAwBAh6AMAAAdgjIAAHQIygAA0CEoA/Cou+9Oqtb2s2fP6u337Mn0lVeubfu9e1dv//KXr/34+/at3v7yy9e+/cc/vnr7pz997dvffvvq7c+w/qq+3H33Y+991epj33772rd9+tNXb//xj699+8svX739vn1r3/7lL1+9/d696x57a95+DMbeGc+jcxx7q37GeOxd3nvt1zv2zkBQBgCADkEZAAA6qrW25pWnpqbagQMH1nXA2dnZTE9Pr2sfk0hf+vSlT1/6zldfqur21trU+isaP+b5jaMvq01PT2d+fj6HDh0aupSRY7z0bfY874oyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIywISrqkur6oGq+sOha9mq5o7N5dpD1+bowtGhS2EMGC+jQ1AGmHzXJ/nC0EVsZTM3z+RL934pM5+fGboUxoDxMjoEZYAJVlWvTjKf5LND17JVzR2by/5D+9PSsv/QflcJOSPjZbRsG7oAADZGVV2c5O1JXpLktWdYb0+SPUmyc+fOzM7Oruu4CwsL697HJHnXHe/KQ0sPJUl+sPSD/Np/+bW84dI3DFzVaJifn8/S0pLxsoLxcmabPb8IygCTaybJ+1trR6rqtCu11vYl2ZckU1NTbXp6el0HnZ2dzXr3MSnmjs3lpv95Ux5qy8HnofZQbvrWTbnhF27Iru27Bq5ueDt27Mj8/LzxcoLxcnabPb+49QJgAlXVZUl+Osm7hq5lK5u5eSbH2/GTli21Jfee0mW8jB5XlAEm03SS3UnuOnE1eXuSC6rqua21Hx+wri3ltiO3ZXFp8aRli0uLufXIrQNVxCgzXkaPoAwwmfYl+eMVv78py8H5mkGq2aIOXn3wkcduSeFsjJfRIygDTKDW2v1J7n/496paSPJAa+2e4aoCGC+CMsAW0FrbO3QNAOPGm/kAAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlANhC5o7N5Yobr8jRhaNDlzJS9IUeQRkAtpCZm2dyy123ZObzM0OXMlL0hR5BGQC2iLljc9l/aH+Ot+PZf2i/q6cn6AunIygDwBYxc/NMjrfjSZKltuTq6Qn6wukIygCwBTx81XRxaTFJsri06Opp9IUzE5QBYAtYedX0Ya6e6gtnJigDwBZw25HbHrlq+rDFpcXceuTWgSoaDfrCmWwbugAAYOMdvPrg0CWMJH3hTFxRBgCADkEZAAA6BGUAAOgQlAEmVFU9uar+tKruq6o7q+o1Q9cEME4EZYDJdX2SxSQ7k/xikt+vqucNWxI86sELH8zXXvw1n1nMyBKUASZQVV2U5FVJ3tpaW2it3ZLkY0muGrYyeNSdu+/Mff/oPp9ZzMjy8XAAk+nZSR5qrd2xYtkXk1xxpo0OHz6c6enpdR14fn4+O3bsWNc+JpG+nOzBCx/M3L+YSyq54S9vyMH3HMyFixcOXdbIMF76NrsvgjLAZNqe5PunLLs3yZNOXbGq9iTZkySPf/zjMz8/v64DLy0trXsfk0hfTnbkBUceeXw8x3PH0+7IJV+8ZMCKRovx0rfZfRGUASbTQpKLT1l2cZJjp67YWtuXZF+STE1NtQMHDqzrwLOzs+u+Kj2J9OVRc8fm8szffWby0IkFFyT3P/v+fOr6T2XX9l2D1jYqjJe+89WXqlrTeu5RBphMdyTZVlWXrlj2giRfGageeMTMzTM53o6ftGypLblXmZEjKANMoNbafUk+muTtVXVRVb0wySuSfGjYyiC57chtWVxaPGnZ4tJibj1y60AVQZ9bLwAm1+uSfCDJt5J8J8k1rTVXlBncwasPJkmmp6czPz+fQ4cODVwR9AnKABOqtfbdJD8/dB0A48qtFwAA0CEoAwBAh6AMAAAdgjIAAHQIygAA0CEoAwBAR7XW1r5y1T1J7lznMZ+S5Nvr3Mck0pc+fenTl77z1ZdntNaeeh72M3bM8xtKX/r0pU9f+jZ1nj+noHw+VNWB1trUph50DOhLn7706UufvowGr0OfvvTpS5++9G12X9x6AQAAHYIyAAB0DBGU9w1wzHGgL3360qcvffoyGrwOffrSpy99+tK3qX3Z9HuUAQBgHLj1AgAAOgRlAADoGDwoV9WlVfVAVf3h0LUMrar+QVW9v6rurKpjVXWoqn526LqGUFVPrqo/rar7TvTjNUPXNDTj4+zMJ6PJ6/Io5/GjzPOrGR9nt9nzyeBBOcn1Sb4wdBEjYluSbyS5Isk/THJdkg9X1e4BaxrK9UkWk+xM8otJfr+qnjdsSYMzPs7OfDKavC6Pch4/yjy/mvFxdps6nwwalKvq1Unmk3x2yDpGRWvtvtba3tba/22tHW+tfSLJ3yW5fOjaNlNVXZTkVUne2lpbaK3dkuRjSa4atrJhGR9nZj4ZTV6XkzmPl5nn+4yPMxtiPhksKFfVxUnenuSNQ9Uw6qpqZ5JnJ/nK0LVssmcneai1dseKZV9MstWvNJxkC4+PVcwno8nrcnZb+Dw2z6/BFh4fqww1nwx5RXkmyftba0cGrGFkVdXjk/xRkg+21r46dD2bbHuS75+y7N4kTxqglpG0xcdHj/lkNHldzmCLn8fm+bPY4uOjZ5D5ZEOCclXNVlU7zc8tVXVZkp9O8q6NOP6oOltfVqz3uCQfyvK9W68frODhLCS5+JRlFyc5NkAtI8f4ONlWnU+GZp7vM8+vmXn+DIyPkw05n2zbiJ221qbP9HxVvSHJ7iR3VVWy/D/LC6rqua21H9+ImkbB2fqSJLXckPdn+c0NL2ut/WCj6xpBdyTZVlWXttb+5sSyF8SfnoyPvulswflkaOb5PvP8mpnnT8P46JrOQPPJIN/MV1U/lJP/J/mmLDfgmtbaPZte0AipqhuSXJbkp1trC0PXM5Sq+uMkLclrs9yPTyb5ydbalp5EjY/VzCejyetyes7jZeb5PuNjtSHnkw25onw2rbX7k9z/8O9VtZDkAZNnPSPJ1UkeTHL0xP+akuTq1tofDVbYMF6X5ANJvpXkO1k+Gbb65Gl8dJhPRpPXpc95fBLz/CmMj74h55NBrigDAMCoG4UvHAEAgJEjKAMAQIegDAAAHYIyAAB0CMoAANAhKAMAQIegDAAAHYIyAAB0CMoMqqpuqqpWVa86ZXlV1Y0nnvvNoeoDYH3M84wz38zHoKrqBUn+V5LDSf5Za23pxPLfSfLGJPtaa1cPWCIA62CeZ5y5osygWmtfTPKhJM9JclWSVNVbsjx5fjjJNcNVB8B6mecZZ64oM7iq+sdJ7khyNMnvJPm9JJ9O8nOttcUhawNg/czzjCtXlBlca+0bSd6dZHeWJ89bk7zy1Mmzqn6qqj5WVf/vxD1tv7LpxQJwzszzjCtBmVFxz4rHv9pau7+zzvYkX05ybZK/35SqADhfzPOMHUGZwVXVa5K8M8t/kkuWJ8hVWmufbK29pbX2kSTHN6s+ANbHPM+4EpQZVFW9LMmNWb6C8Pwsvyv6tVX1o0PWBcD5YZ5nnAnKDKaqXpTkI0mOJHlpa+2eJNcl2Zbkt4asDYD1M88z7gRlBlFVlyX5RJJ7k/xMa20uSU78ue1AkldU1YsHLBGAdTDPMwkEZTZdVT0ryaeStCxfYfjbU1Z584l/f3tTCwPgvDDPMym2DV0AW09r7WtJdp3h+c8kqc2rCIDzyTzPpBCUGRtVtT3Js078+rgkP3LiT3vfba3dNVxlAJwP5nlGjW/mY2xU1XSSz3We+mBr7Vc2txoAzjfzPKNGUAYAgA5v5gMAgA5BGQAAOgRlAADoEJQBAKBDUAYAgA5BGQAAOgRlAADoEJQBAKBDUAYAgI7/Dxp8iNNBwIwOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape='ovr', degree=10, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [0.74081822]\n",
      "Phi(-1.0, 1) = [0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1)\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
    "                    color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=15)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small weight vector results in a large margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.subplot(122)\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x201.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3829069ba8>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 0.20929288864135742\n",
      "[LibSVM]1 0.01 0.22672295570373535\n",
      "[LibSVM]2 0.001 0.2641716003417969\n",
      "[LibSVM]3 0.0001 0.4498331546783447\n",
      "[LibSVM]4 1e-05 0.7316596508026123\n",
      "[LibSVM]5 1.0000000000000002e-06 0.6765744686126709\n",
      "[LibSVM]6 1.0000000000000002e-07 5.2672154903411865\n",
      "[LibSVM]7 1.0000000000000002e-08 0.7318530082702637\n",
      "[LibSVM]8 1.0000000000000003e-09 0.7262847423553467\n",
      "[LibSVM]9 1.0000000000000003e-10 0.7266685962677002\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3828f62160>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dEO0Nw7tPpNKmbxbnNJoxRnB4LMxBS6I9cgxvERkWkS+KyDURyYvIWyLyQjeKo/Zk80Xczhd79tiz3YgIEnEOWhLtVTst7xCAGwCeBjAJ4PMA/puInHSvLNoLv62sbJWIGbi0lEe5anldCpFvOIa3qhZU9RVV/UBVLVX9GoCrAD7ifnnUjmSPnxbvZC4+iVLFwpUs9/Ymatee+7xFZBrA4wCS2zz3kogsiMhCNpvtRH3UhlTaxENHRjE5Gva6lH2xf+mkMtxhkKhdewpvEQkD+C8A/lBVL7U+r6qvqeq8qs5Ho9FO1UgOkukc5mL+7DIBgEePjWMoFOCgJdEetB3eIhIA8GUAJQAvu1YR7clqsYIP7qz5bnFOo1AwgNmZyGb3DxE5ayu8pXYsyxcBTAP4jKqWXa2K2raY8Xd/t22uPuOEe3sTtafdlvfvAjgL4G+o6rqL9dAeJW/5e6aJLREzcH+tjExuw+tSiHyhnXnejwD4xwCeArAkIqv1t191vTpylEybODo+hGlj2OtSDiTB7WGJ9iTkdIGqXgPgjw2iB1CyvrKy1w8cdjI7Y0Ck9vM8m5j2uhyinsfl8T5Wqli4fDvv+y4TABgfDuHU0XFOFyRqE8Pbxy7fzqNcVV/PNGl0lsvkidrG8PYxv6+sbJWIGbhxdx25dU5mInLC8PaxVNrE2FAQp47648BhJ/ZfEItsfRM5Ynj7WDKdw9mYgYBPDhx2whknRO1jePuUZSlSPjtw2MlUZATHJobZ703UBoa3T127u4ZCqdpX4Q3UWt9seRM5Y3j7lN/38N7JXNzA5dt5lCrc25toNwxvn0qlTYQCgjPTE16X0lGJmIFyVXH5dt7rUoh6GsPbp5JpE49NTWA45K8Dh51w0JKoPQxvn0qmzb7rMgGAk0fHMRoOctCSyAHD24dumxtYWS323WAlAAQDgrMx7u1N5ITh7UN2sPVjeAO1rpPFNPf2JtoNw9uH7JkmZ/s1vGOTyBcruHmPW8cT7YTh7UOpjImHj4zBGPHngcNO7EFLdp0Q7Yzh7UPJPltZ2Wp2JoKAAKk0t4cl2gnD22fMjTKu+fzAYScj4SBORyc444RoFwxvn1ncHKzsv2mCjbhMnmh3DG+f6feZJrZEzEA6t4F7hZLXpRD1JIa3zyTTJo5NDGPKGPG6FFfZf1mw64Roewxvn0mmc31zcs5uzsYiALhMnmgnDG8fKVaq+Nnt1b7vMgGAoxPDmDFG2PIm2gHD20cuL6+iYvXPgcNOEnFjc0ESETVjePtIv+7hvZO5uIH3swVslKtel0LUcxjePpJMm5gYDuGRI2Nel9IViZiBqqV4b5l7exO1Ynj7SDJt4mws0jcHDjvh3t5EO2N4+0TVUixm+nMP7508dHgME8Mh7nFCtA2Gt09cu1PAWqmKRGwwBisBIBAQJGIGZ5wQbYPh7RN263MQ5ng3SsQNLGZMWBb39iZq1FZ4i8jLIrIgIkUR+ZLLNdE2kmkT4aDg8emI16V0VSJmYK1UxbW7a16XQtRT2m15pwG8CuD3XayFdpFM53BmKoKh0GD9sbS1tzfnexM1aisJVPWrqvqnAO64XA9tQ1WR6vM9vHdyZnoCoYBwxglRi44240TkpXr3ykI2m+3kSw+0ZbOIO4XSQIb3cCiIx6a4tzdRq46Gt6q+pqrzqjofjUY7+dIDLZWpdRkkBmiaYCPu7U30oMHqQPWp5K1acNk77Q2aRMzA7XwR2XzR61KIegbD2weSaRMnj44h0qcHDjvh3t5ED2p3qmBIREYABAEERWRERELulka2ZCY3UCsrW9kLk9h1QrSl3Zb35wGsA/gXAP5u/ePPu1UUbcmtl3Hj7vrALc5pNDkWxvFDo2x5EzVoq/Wsqq8AeMXVSmhbqQE5s9IJ9/YmasY+7x5nB9Ygt7yB2i+vqysFrJUqXpdC1BMY3j0ulTERjQxjKtLfBw47ScQMqAKXlri3NxHA8O55g7qyshX39iZq1nMzRn732+87Dky1cxSBtHGR0yUKQBWwVKEKKBSWVf8ctWXrlm69t7S2852ltesUW8/br2MpNr+26bGGzxVb3/ODOwX8tbNTbfzE/e34oVFMjoY7vre3qqJQqsJcL8PcKCO3Voa5Udn6fL0Mc73S8HHt+chwCJ+em8bz52Zw4vBgnGxEvaXnwvv63QKSt3YemGpnY1BV56ucrlCt/QIIiECkFvQBka3PRRCQxmukfg22uSaAQAAQSNNrBurPA9L0dY1f//MPH8JnPnyijZ+6v4nsvLf3RrkKc6MWrLl60JoNQVt7vFx/vNLwce35qsN2sxPDIRgjIRijYRgjtZkvt+6v49XXF/Hq64v4uROTeP7cDF44F8OpY+Nu3QKiJtJO0O3H/Py8LiwsuPLaNJh+62sp/NEPP8C545NNQV2qWLt+3XAoAGM0jMnR8GYIT9aD2BgNwRipf97wmP18ZCSEUHD73sVrdwo4f3EJ5y8u4e0b9wEAszMRvHAuhhefnMGZAdu+lzpDRC6o6rzjdQxv8oufXL+Hf/P6IsaGglvBuxm4tWBuDOHJ0Vr4joSDrtd26/463ri4hPPvZHDh+j2oAqej43jhXAwvPDmDRMyAtNOXRwOP4U3kkdvmBv48uYSvv7OEH129A0uBh4+M4YVzM3jhyRg+dGKSQU47YngT9YA7q0V8I7WMr19cwg9+toKKpYhPjuD5eov8Iw8fRiDAIKctDG+iHpNbK+Mbi8t442IG3728glLFQjQyjOfnZvDCuRl89NSRHfvXaXAwvIl6WH6jjG9euo03Li7hW+/exkbZwpHxIXw6MY0XnozhY48eHbgj76iG4U3kE2ulCr7zbhbnLy7hzcVlFEpVGCMhPJuYxovnYvj4mWNdGXSl3sDwJvKhjXIV37+8gvMXl/CN1BLMjQrGh4L41NlpvHhuBk8/EcXYUM8tz6AOYngT+VypYuGHV+7g/DsZ/O/UMu4WShgJB/DJJ6bw/LkZfGp2amAP6OhnDG+iPlKpWvjxB3dx/p0lvJFcQjZfxFAwgPmTh/HUQ4dqbw8fGvgNzPoBw5uoT1mW4ifX7+H8xdo88kuZPCr1Jf7HD402hfm5+CRGh9hf7ifthjc7z4h8JhAQzJ88gvmTRwDU+skv3srhrRv3N99efycDAAgGBLMzka1Af+gQTkcnOLe8D7DlTdSHsvki3m4I87dv3Ee+WDvIIjIcws89NFkP81q3SzQy7HHFZGO3CRFtsizFlZVV/PT6VqBfWspv7qh4/NAonnr4EH6+3jo/d3yS0xM9wm4TItoUCAgem4rgsakIPjv/EABgvVTFxXQOb9mBfv0+Xv9/te6WUEAwG4s0tc4fPTbO7pYewpY3EW26nd/AW9fv4+2bdndLDqt2d8tICB86cahpQPTYBLtbOo3dJkR0YJaleD+7ip/e2Gqdv7u81d1y4nBtdsuMMYKxoSBGh0L190GMDQUxPhTa/Hjz+XDt+eFQgLsrboPdJkR0YIGA4Mx0BGemI/jlenfLWqmCi7dMvHXjHt66cR8/vX4f99ZKWCtV9/baAow1hPtoOIjx4dDmx42/DDZ/IYSDTV8z1vL8UDCAUDCAUFAQDtTehwLSl78kGN5EtCdjQyF89NQRfPTUkabHLUuxUalirVTFeqn2fq1U2fq4XMV6qVJ/vPZc87VVrJcrWC1WkM0Xtx4rVbBWruIgnQThoCBUD/NwMIBQoP5+u88brtvu60LBAIaC8sAvicbX+aUPH8ehsaED3undMbyJqCMCAam3hDsfK6qKjbK1FfjlB385rJeqKFUtVKoWKpaiXFVUqhbKlqJcf7xcVVQsC5Vq7fly1ULFsjavrdSvLVUsFIqV5utbv67+2pWqhdZjUJ9+IsrwJiISEYzWu0aOel3MNiyrMdytruw5w/AmIjqgQEAwHAhiuIuJyt3eiYh8iOFNRORDbYW3iBwRkf8pIgURuSYiv+J2YUREtLN2e2h+B0AJwDSApwC8LiJvq2rStcqIiGhHji1vERkH8BkA/0pVV1X1+wD+DMDfc7s4IiLaXjvdJo8DqKjqew2PvQ1grvVCEXlJRBZEZCGbzXaqRiIiatFOeE8AMFseywGItF6oqq+p6ryqzkej0U7UR0RE22inz3sVgNHymAEgv9sXXbhwYUVEru23sB5xDMCK10X0CN6LZrwfzXg/thz0XjzSzkXthPd7AEIickZVL9cf+xCAXQcrVdX3TW8RWWhnd69BwHvRjPejGe/Hlm7dC8duE1UtAPgqgN8UkXER+QUAfwvAl90ujoiIttfuIp1fBzAK4DaAPwHwa5wmSETknbbmeavqXQC/6HItveg1rwvoIbwXzXg/mvF+bOnKvXDtJB0iInIP9zYhIvIhhjcRkQ8xvA9ARIIi8hUR+ZaI/L6IDPT+6CLyMRH5dv3tPRH5ba9r8pKInBSRbMM98f302f0SkWkR+YGIfEdEvikiMa9r8pKITIrIj0VkVUTO7ec1GN4H87cBXFXVTwK4BOCXPK7HU6r6Q1V9RlWfAfADAH/qcUm94Dv2PVHVQd4zYgXAx1X1aQB/BOAfelyP19YA/HUA/32/L8DwPpjTAN6qf/wTAJ/wsJaeISJDAD4K4Hte19IDfkFEvici/1b68QjzNqlqVVWt+qcROCzy63eqWj7oL/OBCW8Rebm+aVZRRL7U8tx+9ytPAfhU/eNnARzuYMmucul+2J4F8GbDP9ae59L9yAB4DLVf6lPwyV9mbv2/ISJPiciPALyMWmPHF1z+t7Jvg9RHmwbwKoDnUFtw1GjH/cpFZAbAf93m9T4H4GsAnhGRb6LWklhyq3gXdPx+qKr9838WwB+4U7Zr3LofRQAQka8C+CsA/odL9XeSK/dCVd8C8JdF5JcB/EsA/8S1n6Cz3Py3sn+qOlBv9f8IX2r4fLx+8x9veOzLAP7dHl/3FQCf8Prn8/p+AAgDuAgg4PXP5vX9ABBp+PgLAP6+1z+fh/diqOHj5wD8R69/Pi/vR8P1XwJwbj/1DEy3yS7a3q+8lYjM1GeavAmgpKrfdavILtr3/ah7FsA31UddJg4Ocj8+LiIXROR7AI4D+GM3Cuyig9yLp0TkuyLyLQC/AeDfu1Fglx3o34qIfB3ApwH8noj8g71+80HqNtlJ2/uVt9Lanz6fdKMoD+37fgCAqp4HcL7TRXnoIP9/8F7UqeqP0X8D+gf9t/LiQb45W9773K+8j/F+NOP92MJ70czT+8HwbtivvOExx/3K+xjvRzPejy28F808vR8DE94iEhKREQBBAEERGRGRkA7ofuW8H814P7bwXjTr2fvh9QhuF0eKXwGgLW+v1J87gtpqwAKA6wB+xet6eT94P3gveuOtV+8Ht4QlIvKhgek2ISLqJwxvIiIfYngTEfkQw5uIyIcY3kREPsTwJiLyIYY3EZEPMbyJiHyI4U1E5EMMbyIiH/r/Zf57UpbXSFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        w = np.random.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 60000, 0, 100]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56780998] [[[2.28129013]\n",
      "  [2.71597487]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[2.27128546 2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.062485     2.24179316   1.79750198]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, max_iter = 50, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [0.28480668] [[1.05542422 1.09851637]]\n",
      "SVC:                          [0.31933577] [[1.1223101  1.02531081]]\n",
      "SGDClassifier(alpha=0.00200): [0.32] [[1.12293103 1.02620763]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        max_iter=100000, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22QpMoyLZR3321RpI9S864tXKAhNjH00sRDaRBd1FZsnPkMjL1FNm4LxoKZtRbgpgbEwSMkITExP27NnDxIkTAbh58yaOjo4sW7Ys+27u4QFz5xL052WW1v6OYtxi6NEueLR2YV7xT5k/5oKMEhBCCCFEvpevk9WkypYta/hLPDo6ms6dO1OzZk0A/v77b4YNG8a1bBjdXLmBLV0PD8b26ilWJkzI8rs9hR7j3QhwaUvc5i3I2ldC5A3h4eEMGDCAMmXKYGFhgaOjI15eXmzdupUaNWrQq1evVN+3adMmlFIEBf1/6P3atWtp2rQptra2WFtbU716dUaNGsX169dz6nGEELnA0TFj1/MDSVZTUbJkSWbNmkWNGjUAOHToEHPmzMHMLH7hgqCgIK5cuZKl93RwMqFzwoSsbXPjJ2Q1YB8mLVtApUo8nPgt/nNvk03zwYQQWcDb25uDBw+yYMECgoKC2LBhAy1btuTmzZv06tWLn3/+mQcPHjzxvgULFvDKK6/g7u4OwKhRo+jYsSMeHh5s2LCBkydPMn36dEJCQvjxxx9z+rGEEDlItnJ9Ur4es5oRDx48wNraGoD27dtz8OBBLl26hImJCVpr1NMGmTynuIfRmKz7BWbOhL17iaQgay07c/u9gbQdWwtX12e3IURek1fHG965cwc7Ozu2bt1Ks2bNnii/desWJUuW5Mcff6RHjx6G6+Hh4Tg7OzN//ny6du3KwYMH8fT0ZOrUqQwdOjTV+9ja2mbrs+R1efVnSIj8RsasZrHERBVg8uTJzJ8/3zDmtXHjxobxrlnJpKAFdO4Me/aw7eujbCrqS/uoVQxaVJvQ0g35ts5ytm6QHbJE/pDaItmJx9y5/683d+7T6yZVp0766qWHjY0NNjY2rF+/nqioqCfKixYtSrt27Vi4cGGy68uWLcPKyoq3334bgBUrVmBtbc2gQYNSvY8kqkKI/EaS1efg7u5Oy5YtAYiKiqJixYo4JWwt8ejRIz777DPOnz+fpfdsNtyDDjfm8vfmyyyr/S323ODDI12o2boUG2qMRNa+EiJ3mZmZsXjxYpYvX46trS0NGjRg+PDhHDhwwFDHz8+Pv/76K9nY1IULF/Lee+9hZWUFwJkzZyhXrhzm5uY5/gxCCGGMJFnNJEtLS+bPn0/Pnj0BCAgIYOLEiZw6dQqI/8guqyZEKAWeLWzpcngIha+cZmWPLRwt2JC3Tk4GNzdo25bLi7YQeES6WsWLJ7UxXIlHnz7/r9enz9PrJnX4cPrqpZe3tzdXrlzht99+o2XLluzdu5f69esbPnnx8vKibNmyht7VAwcO8M8//ySbeJWfhmYJ8TyyazvSvLjNaV6M+XlIsprFGjZsyOXLl2nevDkQP3HC2dmZy5cvZ+l9HEvET8jyuvcrcWdDYMQI2LcP554tsKpTie/LyoQsIXKDpaUlzZs357PPPmPv3r306tWLsWPH8ujRI5RS9OjRg6VLlxIbG8uCBQuoWbMmdev+f8iWu7s7586d49GjR7n4FEIYr+zajjQvbnOaF2N+HpKsZgNHR0fDygFvvfUW33zzDc7OzkD8LN8hQ4Zk2b3MzMDMzRUmTIBLl1jdegW3TO354PxQ3uzrzBpbP2b0OsrFi89uSwiR9apUqUJMTIxhHGuPHj24du0a/v7+rF69Gj8/v2T1O3fuzIMHD5gxY0aq7d25cyfbYxZCCGMiyWo2q1ixIoMHDzacR0REEBERYThftmwZwcHBWXMzCwveXd+Z6nf38Mvoo2wq6kP7qJW8vzB+QtYOv+VIV6sQ2ePmzZs0bdqU5cuXc/z4cUJCQvD392fKlCl4eXlRuHBhIH7b5xYtWjBgwAAeP36Mj49PsnY8PT35+OOP+eijjxg6dCh79uzhwoUL7Nixgy5dujB9+vTceDwhhMg1snRVLrpz5w4ODg589NFHTJgwAa21YfmbrKA1HPjjDkEjF9Pg6CwqcAbs7cHPj6DX+uLwUmlkYrEwNnl12aHo6GjGjRvH1q1bOXv2LNHR0Tg7O9O6dWtGjx5N0aJFDXXXrl2Lt7c3nTt3ZsWKFam25+/vz8yZMzl69CgxMTGULVuWtm3bMmTIEOzt7XPqsfKkvPozJNInu7YjzYvbnObFmJNK79JVkqzmstDQUMzNzXF0dOTAgQM0btyY33//PdV1GjPj9s047I78F2bORP/2G3FxsMn0Lc6+PpAm45vhUVs62YVxkERDZJb8DL3YJFn9v7wYc1Kyzmoe4eLigmPCHmoODg4MHDiQl156CYD169czaNAg7t+/n+n72BUzgebN4ddfiTgewuoyI6gXu48hm5JMyJonE7KEEEIYt+zajjQvbnOaF2N+HpKsGpGyZcsybdo0ihQpAsDp06fZsmWLYUOCvXv3cjELZkoVquqKT8gEbh+7xNLXl/9/QlYfZ/xte3Nq5dFM30MIIYTIDtm1HWle3OY0L8b8PGQYgJGLiYnBzMwMrTVVqlTBwcGBnTt3AvD48eMsWTg8IgL+mBwIs2bS8tYKrHgIDRrAwIGcqfk25apYYCJ/1ogcIh/hisySnyEh8gYZBvCCSFwCSynFxo0b+e677wB4+PAhpUqVYs6cOZm+h40NeH/pQYcb87h5/Ap8+y3cuAG+vtjWKMVc+5EsHHsRWTFHCCGEEDlNktU8pGzZstSqVQuIXwKrQ4cOVK1aFYCLFy8ybNiwTG0+oBSUqm4LQ4bA6dOcmbmFwxYN6X1rMt3GlWV38XZ812oLx47KDllCCCGEyBk5mqwqpd5XSgUopaKVUoufUq+7UipWKRWR5GiSpLyMUmq7UipSKXVaKZW1U+fzAHt7e2bNmsXLL78MxG/bOGPGDMOuNxcuXODq1avPfwMTEyoMaE6z+7+y5cdgVpcegWfsXoZsakHB2vETsu5duJ0VjyKEECIPMJatPVOLIfHITMzZ+Xympqm3bWqa+bbzg5zuWb0CjAcWpqPuPq21TZJjR5KyVcBRoBgwClijlMrXCw927NiR8PBwypYtC8CXX35J5cqVDcnr845NNjODlv1K43N+ArcCk0/IKlzZGXr3hsBArl/PskcRQghhhPLi1p4ZiTk7ny8ujQ8k07ouksvRZFVrvVZr/Stw83nbUEq5A7WBz7XWD7XWvwAnAO8sCjPPStwhB+Djjz9mwYIFFChQAIB27doxZsyYTLVfqaYFXf/wodqdPVxYdwR8fGDFCqhVi3OODfm27gr+uzE6T6ztJoQQQoi8wZjHrNZSSt1QSgUppcYopcwSrlcFgrXWSRcfPZZw/QlKqT4JQw8CwsPDsztmo+Hu7o63d3z+Hhsbi4ODg2FnrLi4OCZOnMi5c+eeq20bGyjdrhbMmweXL7P/3W8pxg0+POxL9TdLMbe4TMgSQgghRNYw1mR1F1ANcCC+x/Q94KOEMhvgbor6d4FCqTWktZ6rta6rta6bX7coNDU1Zd68eQwdOhSAkydP8tlnn7F//34AIiMjee5E3s6O+quGUPjyaZZ33cIRy4b4JZmQNdt7i3zOIYQRWLx4MTY2Njl2P6UUa9asMZyfPn2aBg0aYGlpSZkyZVKtI4QQqTHKZFVrHay1DtFax2mtTwBfAG8nFEcAhVO8pTCQ+W2e8olq1aoRGhpKhw4dAPj5558pWbIkp0+ffu42nUqa4Lvk/xOyfir9CZ6xe+m3tgVUqgTffcfj8DskDKEV4oUUHh7OgAEDKFOmDBYWFjg6OuLl5cXWrVsNdYKDg/Hz86N06dJYWFhQsmRJXnvtNZYsWWIYYw7xiVziYWVlhZubG507d2b37t2p3nvt2rU0bdoUW1tbrK2tqV69OqNGjeJ6Lg0ov3r1Kq1btzacjx49GisrK06fPs2hQ4dSrSOEEKkxymQ1FRpInOf3D+CmlErak1oz4bpIJycnJwoWLAhAo0aNGDduHBUrVgTg66+/pn///sQ9R49o4oSszucncivwEuHfLofixeHDD1EuJfm5SG9m+AVy6VKWPo4QRsHb25uDBw+yYMECgoKC2LBhAy1btuTmzfhh+gEBAdSqVYu///6bH374gRMnTrBr1y4GDBjAkiVLDElconnz5nH16lVOnTplGIP+6quv8vXXXyerN2rUKDp27IiHhwcbNmzg5MmTTJ8+nZCQEH788ccce/6knJycsLCwMJyfPXuWl19+mTJlypD4KVfKOhkVExPz3JNHRcblxa09MxJzdj5fWhvryIY76aS1zrEDMAMsga+AZQmvzVKp1xJwTHhdCfib+AlVieX7gW8S3t8euAPYP+v+derU0eLZPvnkE92uXTvD+Zo1a/TZs2cz1+iRI3p7eT/9gIJag95DA/1t3eV62+9ROi4ukwGLF8rJkydzO4Tncvv2bQ3orVu3ploeFxenq1SpouvUqaNjY2PTrJMI0P7+/k/U+fTTT7Wpqak+c+aM1lrrAwcOaEBPnTo1zbi01nrRokXa2tracP3s2bO6TZs22tHRUVtZWelatWrp3377Ldl7f/nlF129enVtaWmp7ezsdOPGjXVYWJjWWuuLFy/qNm3aaDs7O12wYEFdsWJFvWrVqlTjJ77DwXB8/vnnqT5jaGio7tSpk7a1tdW2tra6VatWOigoyFD++eef66pVq+pFixZpNzc3bWJiou/fv//EM+fVnyEh8hsgQKcjf0yctJRTRgOfJzn3BcYppRYCJ4EqWuuLgBewWCllA1wDlgMTk7zvXWAxcBu4CLyttc4/s6ey2aRJkwy9FVFRUXTv3h1fX19DD839+/cpVCjVIcJpq1WLV4PmsW/jFM6MXkKDwFkMCfDl+psfMqdob0qO60ub912z+lHEi2LIEAgMzNl7enhAwo5x6WFjY4ONjQ3r16/n5ZdfxtLSMll5YGAgJ0+eZNWqVZik0Z2iUlsoMoVhw4YxadIkfv31V4YPH86KFSuwtrZm0KBBqda3tbVN9XpERAQtW7Zk/PjxFCxYkJ9++okOHTpw/PhxKlWqRFhYGO+++y5fffUV3t7eREREGMa5AwwYMICoqCi2b99O4cKF+ffff9OM+erVqzRp0oS33nqL4cOHpzp2NjIyktdee42GDRuyc+dOChQowDfffEOzZs04deoUVlZWAISEhLBy5Ur8/f0pUKDAE19nIcSLJ0eTVa31WGBsGsU2SeoNB4Y/pZ3zQJOsi0yklPiPpqWlJadOnTIMCThz5gzVq1dn1apVtG/fPoNtQsM37Wj45hCuXv6AFSO3UfznmfS+NQn1wSTY1hoGDiS2iRem5vLZiMhbzMzMWLx4Mb1792bu3LnUqlWLRo0a0bFjRzw9PQkKCgIwDLcBuHv3Ls7OzobzkSNHMnLkyKfep1ixYjg4OBAcHAzE/z9Zrlw5zM3NMxRvzZo1qVmzpuF81KhR/Pbbb6xZs4bRo0dz5coVHj9+zNtvv03p0qWB+PHuiS5cuIC3t7ehjcQ1nlPj5OSEmZkZNjY2OKWxwvrq1avRWrNo0SLD7585c+bg4ODAhg0beOeddwB49OgRy5Ytw9GYP3sWQmSpnO5ZFXmQi4uL4bWFhQX9+vWjXr16AOzcuZM1a9Ywbtw4ihYtmu42Szib4LPkdR7Pf50tCy7Q9MwcLJbNh//8h1t2FVhpOwDnkd1p09WWhKViRX6WgR7O3OTt7c2bb77J7t272bdvH5s3b2bq1KlMmDCBcuXKPVG/UKFCBCb0GLdq1SrZBKun0VobErrET0Ey6sGDB4wbN44NGzZw9epVHj9+TFRUFDVq1ADik9lmzZpRrVo1Xn/9dZo1a8bbb79tGG86ePBg+vXrx+bNm/Hy8qJ9+/bUqVPnuWIBOHz4MCEhIU98ahMZGZlsmT0XFxdJVIXIZ6T7SmSIq6sr3333naE36MSJE/zyyy9YW1sDcOTIES5evJju9szN4ydkWUydCJcuEbd0ORcjizM45ENa9S7Jz7a9mdlbJmSJvMPS0pLmzZvz2WefsXfvXnr16sXYsWMNyzUlXXXDxMSE8uXLU758ecMGHs9y48YNwsPDcXNzA+LXVD537ly6E91Ew4cPx9/fny+//JKdO3cSGBhIvXr1DO2YmpqyZcsWtmzZQo0aNViwYAEVKlTg2LFjAPTq1YuQkBB69OhBUFAQDRs2ZOyLisaZAAAgAElEQVTYsRmKIam4uDg8PDwIDAxMdgQFBdG3b19DvcTfNfmNsWx1ml2yazvSjHzdMhLDi/79MDaSrIpMef/99zl//rxhRu/777/PW2+9ZSiPjY1Nf2MWFph08cE9fC/+nx5ho60PHR6uYOD8WlxybcR3L63g+KHorH4EIbJVlSpViImJoVKlSlSuXJkpU6Zk7P+LFKZOnYqJiQnt2rUDoHPnzjx48IAZM2akWv9OGrtz/PXXX3Tt2hVvb29q1KiBi4vLExuFKKVo0KABn3/+OYcOHaJkyZL89NNPhnIXFxf69OnDzz//zBdffMHcuXOf+7lq167N2bNnKV68uCGBTzwy8qnNiyovbnWaEdm1HWlGvm4ZieFF/34YG0lWRaYl7RFavnw5s2fPBuIT1UqVKjFt2rQMtVeoEHScWAvvW/MI3HCZpR7TsOc6QwJ8qfR6KRg1Ci5elG1dhVG5efMmTZs2Zfny5Rw/fpyQkBD8/f2ZMmUKXl5eFClShMWLF3Pu3DkaNGjAf/7zH4KCgjh16hTz588nNDQU0xRdOHfu3CEsLIyLFy+yfft2unfvzuTJk5k0aZJhWIGnpycff/wxH330EUOHDmXPnj1cuHCBHTt20KVLF6ZPn55qvO7u7qxbt44jR45w4sQJfH19iYqKMpTv37+f8ePHc+jQIS5evMj69eu5dOkSVapUAeKHAWzevJng4GACAwPZvHmzoex5+Pj44OjoSNu2bdm5cychISHs2rWLYcOGcebMmeduVwiR98mYVZGl3NzcDB9PRkRE0Lx5c9zd3QG4ffs2EyZMYNCgQYYJG0/z/wlZH3L18mDWf7WN1pdmwqRJMGkSh5xas6/WQJpO8KJ6Tfm7S+QuGxsb6tevz/Tp0zl79izR0dE4OzvTuXNnRo8eDUC9evU4cuQIX331FYMGDSIsLIyCBQtSo0YNJkyYgJ+fX7I2e/fuDcSPFS9RogT169dnx44dNG7cOFm9yZMnU7duXWbOnMmCBQuIiYmhbNmytG3blgEDBqQa77Rp0+jVqxevvPIKdnZ2DBkyJFmyWqRIEfbs2cMPP/zAnTt3KFWqFGPGjMHX1xeI/9h+0KBBXLp0iUKFCuHl5cXUqVOf++tnZWXFrl27GDFiBB07duTu3buGDRMSt4oWQuRP6nkH5+dFdevW1QEBAbkdRr61efNm2rRpw4EDB6hVqxZhYWForSlRokTGGrpwgXtfzyFq5nwcCOdf3Nni1h/nUd15y1cmZOV1p06donLlyrkdhsjDXsSfoaetavYi/DOeXc+XkXazq65Im1LqsNa67rPqSXeUyDFvvPEGYWFheHh4APE9O2XLluXevXsZa6h0aQrPmMjNo5dY0nw5d0yKMSj4Q1r0cuZn2z7M6hPIrVvZ8ABCCCGEyHGSrIocVbRoUcOSO35+fsydO5fChQsD0LNnTz766KN0t1XZw4JuW3yocid+QtYm2850eLicAfNqUeiNRrBiBUTLhCwhRN6XF7c6zYjs2o40I1+3jMTwon8/jI0kqyLXuLu707VrVyB+rciCBQsm241m+vTp6ZpYkXJC1l/e0zC/fR18fdGurqwsO4rFX1wkox24QghhLMLC4j9eTnmEheV2ZFkjNjb158vEwhlAxr5uGYnhRf9+GBsZsyqM0sWLF3Fzc+Pbb79l0KBBPH78mDt37hgWJH+muDjYto2ro2ficGgDABtNWxP8xkC8JnpRrYb8nWasXsTxhiJnyc+QEHlDlo9ZVUpZKaUaKqXaKaU6JD0yF6oQT3J1dSU0NJRu3boBsGnTJkqWLMnBgwfT14CJCbz+OsX3/Ic/ZgWz2vUTPGP3Mvj31zGvWZkfyn3H2oV3Mv1Xu8ge+emPaJG15GdHiBdPupJVpVQz4ALwF7AWWJPk8M+26ES+5uTkZBjPWrVqVT755BNq1aoFwIIFC+jXr98zd+0xN4dW/UvjcyFhQlazZYYJWW/4OWPSrw8kbHcpjIO5uTkPHz7M7TBEHvX48WPMzLJuVca8uFNRavEmHillZNemjO4ylV07QmVXXWG80tuzOh34HXDRWpukODK5GZoQz1auXDnGjx+Pubk5AJcvX+bUqVOGDQk2bdr0xO47KVX2sKDbVt/4CVkjDnOl8XuoFcuhVi2iX2rE9Por2fFHtCw7ksscHBy4fPkykZGR0ksmMiQuLo5r165RpEiRLGvzRd+pKCO7NmV0l6ns2hEqu+oK45WuMatKqQdADa3107MBIydjVl8sWmuUUsTGxuLs7Ezjxo35+eefAYiMjMTKyurZjdy+DYsXc3P8LIrdOss1HPi1mB8WH/SlwxBXEjp2RQ67d+8e169f5/Hjx7kdishjrK2tcXFxwSSz08gT5MX1NI1lbVFjiCMvfv/yk/SOWU1vsroF+E5rvTErgsstkqy+uEJDQ3n48CEVKlTg2rVrlCtXjtmzZxt223mWK6Fx/PfTbTj4z6RZtEzIEkLEy4vJjrEkfsYQR178/uUnmZ5gpZSqnXgAs4FvlFJ+SinPpGUJ5ULkKhcXFypUqABAbGwsvXr1ok6dOgAEBgbywQcfcO0pn/uUdDGhy7LXaXo/cULWx9SP3cPg31/HrlFlmD4d7tzJkWcRQgghxP+l2bOqlIoDNPCUv0sA0Hll3Kr0rOZPixYt4sMPP+T8+fPY2tpy8uRJbGxscHV1fer7Th6N5tDH/rS7Oosi/+wDKyuuNvVhXcmBtP2sJs7OOfQAQohckxd75oyll9IY4siL37/8JCuWrioLuCX892mHWwaCel8pFaCUilZKLX5KvW5KqcNKqXtKqVCl1BSllFmS8h1KqSilVETC8W96YxD5T48ePQgLC8PW1haAjz/+mJdfftkweScujVkAVWrFT8gq8vdeOHwY3nuPopuWM2CuBxdcGjHdUyZkCfGie9F3KsrIrk0Z3WUqu3aEyq66wnild8xqY2Cv1jomxXUzoKHWele6bha/Jmsc0AIoqLXunka9/sDfwAHAHlgP+GutJyWU7wCWa63np+e+iaRnVQAEBwcTHBxMs2bN0Frz0ksv0b59e0aNGvXM9+7ZcJuzoxfT8NgsKpAwIau4H5Yf9KX9YJmQJYQQQqRXVm8KsB0omsr1Igll6aK1Xqu1/hW4+Yx6P2qtd2utH2mtLwMrgEbpvY8QT+Pm5kazZs0AiIqK4qWXXqJ06dKG8xEjRhAcHJzqexu9ZUe3wA+xvvQvy3w2E2hRH78bk/D9rCxXPNvD1q1pr9cihBBCiAxLb7KqiB+/mlIx4EHWhZOmxsA/Ka59pZS6oZTao5RqktYblVJ9EoYeBISHh2drkCLvKViwID/++KNh1YCAgACmTZtmSFZv3brF1atXn3hfSRcTuixvQdP7/2HzzGB+Kv0xFa7/Ba+/DpUrc7THdNYtuoOsvCSEEEJkzlOHASil1ie8fBPYBkQnKTYFqgGntNZvZOimSo0nfoOB7umo2xP4AvDQWt9IuOYJnAQeAe8CMxLKn7oOrAwDEOlx48YN7OzsMDU1ZdKkSYwePZrQ0FCcnrXlSXQ0+PujZ85E7d/PA6xYV9CH+10H0maMTMgSQgghksqqYQA3Ew4F3E5yfhMIJX5Jq/QtZPkclFLtgK+AlomJKoDW+oDW+r7WOlprvQTYA7TKrjhE/lK8eHFME/YD9Pb25ocffjAkqsOHD2fQoEGpv9HCAnx9ebxzHz99cphNRd6jw8Pl9J/jwXmXl5nuuZKdW2RClhAi44xhi9Hs3LrUGLZFNYYYROrSO8Hqc+AbrXWWfOSfnp5VpdQbwDLgTa31wWe0twnYpLX+/mn1pGdVZNbQoUOJjo5m5syZACxYsIBXXnkFd3f3J+pqDX/9dptzYxbT6Pj/J2Td7+RH+Sl94RlLZwkhRCJjWK4pO5eBMoYlpowhhvwmS3ewyioJqweYAZ8DLkBvICaVVQaaAv5A+5QrDSilbAFPYCcQA3QC5gK1tNZBT7u/JKsiK926dQsnJydGjhzJ2LFj0Vpz8+ZNihcv/kTdy5fi+O+IrZTZOItX7m2IX7y4TRu2VBiIc1cvqlZ71nLGQoj8TJLV7GcMMeQ3mU5WlVIhpD6p6gla63SttaqUGkt8oprUOGAh8WNQq2itLyqltgOvAFFJ6u3WWrdUStkDG4FKQCxwGhijtd76rPtLsiqyWlhYGGZmZhQvXpw9e/bQpEkTNm/ejJeXV9pvOn8e5swhbt58TG7e4F/c2VJuAC6juvGWry3m5jkWvhAij5BkNfsZQwz5TVYkq8OSnNoAQ4GDwL6Eaw2AesBUrfUXmQs3Z0iyKrLT+fPn+fHHHxkzZgw2NjasWbOGbdu28c0332BjY/NE/ashUfzRew2Vt8/EMy75hKy2n9WkZMlceAghhFGSZDX7GUMM+U2WDgNI2G0qSGs9McX1T4GqWutsm2SVlSRZFTnp66+/ZunSpRw/fhylFDt37qRUqVK4uSX/IOLePdg04Qimc2bR6u5KrHjIHtWIajMHUKTX21CgQC49gRDCWEiymv2MIYb8JquT1XtAba312RTXywNHtNZ5Yt8eSVZFTouLi8PExAStNRUrVsTV1ZVt27YBEB0djYWFhaGuYULW6EW8fu5HSkaeBQcHtF9v1jn0pXnPUhQqlFtPIoTITU5OcO3ak9cdHSEs7PnrZlcMxtR2Xoohv8nqZPUq8eNC56e47geM11rniYUdJFkVuSk0NJTbt29TvXp1IiIiKFOmDBMmTKBv375P1I19HIfpn1th5kz0hg3EacVG0zaEtBqI10SZkCWEECLvy+rtVr8FZiqlZiuluiccs4EfEsqEEM/g4uJC9erVAXj48CE+Pj54eHgAEBwczAcffEBoaCgApuYm0KIFrF/PEf9gVpX6mPqxf/HBb80xq16JGRWm8+ti2SFLCCHEiy/dS1cppd4BBgOVEy6dAqZrrX/OptiynPSsCmP1888/0717d4KCgnBxcSE4OBhzc3NKlSplqPN3QBSHPllDlR3/n5C1qagv3v8dgPKomYvRCyGEEBlnlOus5jZJVoUxe/DgAdbW1gB07dqVDRs2cO3aNczNzdFaoxJG/9+9C5snHsFszkzaPFiJeUwUNGpEZM+BBLh684pXgadOFBBCCCGMQVYPAxBCZLPERBVg7NixLFmyBPOERVdbtmzJqFGjAChSBDpNrk2H2wuIvXAZpk6Fa9ew6tWZis1LMddhNEsnXOL+/Vx5DPGCkK0ns58xbKEqRF6QZrKqlLqnlCqe8Pp+wnmqR86FK0T+4ObmRuvWrQGIjY3Fzc2NEiVKGM7Hjh3L2bNnsCxZFIYOhX//ZcOgzQQW8KT3jYn4jC7DjqLt+aHtNk7+k38+PRFZJ7VZ0U+7LjIuI19j+X6I/OxpmwJ0A1ZrraOVUt15ym5WWusl2RNe1pJhAOJFcOzYMerWrcuKFSt45513iIiIICIiAicnJx49gj/mnOfulDm0CJ2PPTc4TUVOvdqf9r92A1vb3A5f5BGy5mT2M4a1U4XITTJmNRWSrIoXRXh4OIULF8bCwoJ58+bRr18/Tp06hbu7u6HOiUNRBIzwp8qOWXjG7QcrK/D15Zr3AOKq1ySho1aIVElylP0kWRX5XZaOWVVKjVRKNVBKmWU+NCFEZtnb2xs2FGjatCmTJ0+mQoUKAEyYMIE+ffpQra4FPf7bhUq39vF4/2F4911YuhTHFh4El3yZ7xusYvd/H8k/dEIIIYxaeidYtQS2A7eVUlsSkteGkrwKkfvKlSvH8OHDDasFPHjwgPv37xvON25cRbCtNSxYgA69zMo6U3EkjA/2d8a9WSnmOY5m2USZkCWEEMI4ZWSd1YJAI+BVoAlQF4gB9mqtW2RXgFlJhgGI/Obhw4fY29vTs2dPvv/+ewDu3LlDxL3C/PnpVhzXzKT5ow1oFL+btaXwJwNo8qXX0z9zFPmCbD2Z/YxhC1UhclO2jVlVSjkCTYE3gXeAGK211XNFmcMkWRX50dWrV4mLi8PZ2ZmTJ0/i4eGBv78/bdu2TXVCFhUrQv/+hHp1w6mSLWby+YkQQohskNVjVt9RSs1SSp0CgoHewBmgOWCXqUiFENmqRIkSODs7A1CoUCEGDx5M/fr1Adi1axu//zOJVsc+4t7fl2Dp0vgVA4YMoVhNZ1YX6cvsAce5ejU3n0AIIUR+lq6eVaVUHBAOfAPM1FpHZndg2UF6VoVIbsaMGXz99decOXOGAgUKcPDgQYoXL06x87fZ1mEWre6upCBR7KERh+sPpPYEbxq9JjtkCSGEyLwsHQaglPIjfqzqq0BhYDewg/hJV0d1Hln/SpJVIZ4UExODWcJn/Z6enjx+/JgjR46gNWz/5ToXv1jOyydmUZ5zXMOB/9j35rXVfanQtFQuRy6EECIvy9JhAFrr+VrrLlprV6AO8CvwErAPuJGBoN5XSgUopaKVUoufUfdDpVRYwi5ZC5VSFknKyiiltiulIpVSp5VSzdIbgxAiObMkg1L9/f2ZPXs2ADExj+kx7CVudQfLC0EseW8TgQU88QufSPnmZaBDB9i2jVs388TfqiIPMjVNfYtRU1PjbBeMY1tU2cZVvGjSu3QVSikTpZQn8DbxE6veAhQQlIH7XQHGAwufca8WwAjACygNuAHjklRZBRwFigGjgDVKKfsMxCGESIWrqyv16tUDIDIyknbt2lG1alVcXE148/u6/Na7PNvn70N99BHs2gXNm3PDoTIz3L/nP0vvEhOTyw8gXihxcRm7ntvtgnFsiyrbuIoXTXqHAWwCGgIFgcPEDwHYAfyltX6Q4ZsqNR5w0Vp3T6N8JXBeaz0y4dwLWKG1dlJKuQMngOJa6/sJ5bsTymc/7b4yDECI57dx40Y6dOhAQEAA1apV4/K5c1yZvgk9czn14g7wACvWWfnyoPtA2o6pIT0zItOya9em7NwNyhh2mpKdsURekaXDAIBA4ntT7bTWDbTWn2qt/3ieRDWdqgLHkpwfAxyVUsUSyoITE9Uk5VVTa0gp1Sdh6EFAeHh4NoUrxIuvVatWXL9+nWrVqgHwzYwZNJ73Ec4Xt7FqeACbinTCO3IpfWfV5FyJ+B2yIu88yuWohRBC5HXpWkFRa/1pdgeSgg1wN8l54utCqZQlljun1pDWei4wF+J7VrM2TCHyl8KFCxteDxo0iPr16+PsbMN7X9fhvdAofo8cz6vB8PLfP/LB/s7oSo7Quzf06UOUfSksLXMxeCGEEHlSuses5rAI4lcdSJT4+n4qZYnlslmkEDnIzc2NTp06AaC1xt6+OG51I+l+YhgFQv7lxzbDeFClCkyYgC5blq2FOjCj/X85fUr+ZhRCCJF+xpqs/gPUTHJeE7imtb6ZUOamlCqUovyfHIxPCJGEUorvv/+eMWPGABCrL/LBxuksf+cdCA7mwCtDaRCzi/d/bQZV4idkrV8mE7LE05mk8S9UWtdzu12I3/40I9ezQ0ZiMIZ4hXiWHE1WlVJmSilLwBQwVUpZKqVSG4qwFOillKqilLIFRgOLAbTWQcSPof084f3tgRrALznyEEKIZypbtixXrlzB19cXypTh8vuelMKF72rP4J6JLe+fGYxX15KsLtKXxUOP53a4wkjFxsZP8kl5xMYaZ7sAYWGptx0Wlvm2syMGY4hXiGfJ6Z7V0cBD4pel8k14PVop5aqUilBKuQJorTcDU4jfdOAicAH4PEk77wJ1gdvAJOBtrbXMnhLCiNjb22NjYwNA7dq1GfmFN+8f6Iv7zf184jWfteZt8Y5cSvdva8Irr8CqVcRFPZIZyEIIIZJJ19JVLwpZukoI4/DFF1+we/ceRvVfRZUDi7Be+g3WYWE8LOLIMoveWA3pQ7tBpUjIdYUQQryAMr3dqlLqPpCuTFZrnXLCk1GSZFUI46G1RilFbGwsJZ2cGFa9Oq+dtKbOtd+Jw4RNZm248NZAmk1sSqXKT1kMUgghRJ6U3mT1aUtXvZ+F8QghRDIqYTVyU1NTAo8f5+HDhzg7u7FsUiBhY6fSK2YTrX9dx+lfKzLDfQDVpnSjSdsiuRy1EEKInJZmsqq1XpKTgQgh8q8SJUoYXr/ex5Gp9x3Z57mH8FkHqbZzBu8HDebROyOhhy8MGAA1auRitEIIIXKSjFkVQhi1mTNXsGr4n2xu8wib9WsgKopzJevze+nB1JnQgYZNCjx1y0ghhBDGKdNjVlM0VgAYBbwHuALmScu11qbPGWeOkmRViLzp0aNHFChQAG7dYm6DRjQNiqI85wnDkfUOflh92I9277vIhCwhhMhD0puspnfpqi+BbsBUIA74CJgJ3AQGPG+QQgiRHgUKFIh/UbQob2z9g6OrV7K40yaOFXgJv+sTeffTMmy368DMDv/lfEj++bRICCHyg/T2rIYA/bXWmxNWCfDQWp9TSvUHvLTWb2d3oFlBelaFeLHcvh3J4LY/8vLfF+hweyXFucnlQo5YDOlD8WHDoIhMyBJCCGOV1T2rjsDJhNcRgG3C683A6xkPTwghMs/Ozoqlu4bR59b3hO4LZU7DaYRGXKf4l1+CszN/uvdmave9XLuW25EKIYR4XulNVi8CJRNenwVaJLxuQPwuVEIIkas86lvSd8+HuN+8SdzBg9xv9Q4Nzixn2JJGnCnxCj80Ws2e7bJDlhBC5DXpTVbXAV4Jr6cD4xKGBiwG5mdDXEII8Vzs7OwweeklrFcvZP3MAKaVHIWTvsqgve9RrqkrU60Hs2JyKJGRuR2pEEKI9HiupauUUp5AIyBIa70hy6PKJjJmVYj86UJIHH+O2ILjmum8EfcHcZgQ82Zb/nBzxr1fPypXqZLbIQohRL6T1UtXNQb2aq1jUlw3AxpqrXc9d6Q5SJJVIfK36GjYNCsEh7WzqX9yPia3bnGjuD0by4zB9gNf6jR9hLOzY26HKYQQ+UJWJ6uxQAmt9fUU14sB12WdVSFEnhMVxb3583n03RKKnwsgAmuW044r7XwYOLsljpKzCiFEtsrq1QAUkFpWWwx4kJHAhBDCKFhaUvj99zE9dIiVQwPYZPM23fiFL35txZkSr/BZxR9o1+pLIiLkV5wQQuSmp/asKqXWJ7x8E9gGRCcpNgWqAae01m9kW4RZSHpWhRBpiYuDnWtvEvL5Yl49OYtyBBOGPQ4j+2DSvx/bTp+mVKlSVKxYMbdDFUKIF0JW9azeTDgUcDvJ+U0gFJgN+GYuVCGEyH0mJvDa28Xo+c8wzILPsPidjURV88Tkq4noMmV43OYdxjVeTdC/8X/gP3woq/YJIUROSO+Y1c+Bb7TWefrzMOlZFUJkWEgIzJ7Ngx/mY/3wFqeoxLYKfZkY+hdjvnmdAQP65HaEQgiRJ2XpmFWt9Tit9QOlVF2lVCellHXCTawTVgRIb1BFlVLrlFIPlFIXlFKd06i3SSkVkeR4pJQ6kaT8vFLqYZLyLemNQQghMqRsWZg8meCdl1n46hIiVGEGnfmQMw83YzP8EPM+OMG+fefo378/ly5dyu1ohRDihZOuRFMp5Qj8B6hH/ESrCkAwMA2IAgan834zgUfEb9/qAfyulDqmtf4naSWtdcsU998B/JmirdZa623pvK8QQmRK9Zcsqb6jK7dvd2XllwEUmD+LjveXU/CH+RxZ5MnDx4GYjRgBwJkzZzAzM6Ns2bK5HLUQQuR96V0N4FvgGvGz/5Pu++IPvJ6eBhJ6Y72BMVrrCK31X8B6oMsz3lcGeAVYms5YhRAi29jZQedpdelwZyH7/UNZVOVrKhUKZ3F0NCU8PbnccwxD31lL3bqNiYmJX5o6Li4ul6MWQoi8K73JqhcwSmt9O8X1c4BrOttwB2K01kFJrh0Dqj7jfV2B3Vrr8ymur1BKhSultiilaqb1ZqVUH6VUgFIqIDw8PJ2hCiHE0yVOyOrxz3CsQs/Axo1Qty4lFk1gXeAoFt6ty5x3d3EmSOPl5cWoUaNyO2QhhMiT0pusFiT+4/uU7IkfBpAeNsC9FNfuAoWe8b6uwOIU13yAMkBpYDvwh1LKNrU3a63naq3raq3r2tvbpzNUIYTIABMTaNkSNmxgw3fnWFViGI1idzPwFy9iKlah8bGGPAyrQ2wsxMTE8Omnn/Lvv//mdtRCCJEnpDdZ3QV0T3KulVKmwCfAf9PZRgRQOMW1wsD9tN6glHoZcALWJL2utd6jtX6otY7UWn8F3CF+qIAQQuSqNoPL0uXKZC7tDWVB4/gJWeNuT+SLhV051rA/Z9auZdq0aZw8eRKAe/fuERoamstRCyGE8Upvsvox0FsptRWwAKYCJ4FGwKfpbCMIMFNKVUhyrSbwTxr1AboBa7XWEc9oWxO/FqwQQhiFWg0s6bWzK+VvHmDlh4f4o8g7eBxbTOVOnYioU4fS+x6wf9cjli1bjqurK+fOncvtkIUQwiild+mqk0ANYB+wBbAkfnJVLa11un7DJqzRuhb4ImHJq0ZAW2BZavWVUgWBd0gxBEAp5aqUaqSUKqCUslRKfQQUB/akJw4hhMhJhglZt//X3n2HR1mlfRz/3kkghASktwChiVJFERFpcS2rC6ggCC4CKog0dVVUfHUVxFXEsrorwVWqCgK2FQWsEHRZFrCASDEgvSQEqYEQUs77xwwhhAQSyGQmye9zXXPBnOeZ57nPjIk3Z8459xSCdu6AF18kJCGBy17sR1TnKILG7GBot5nUrNkQgKeffppBgwaRlz2wRURKgjzvkeqc2w08dZ73GwZMAfbgqYI11Dm3xsw6AguccxFZzr0Fz9f7i7JdoxwwEWiIZ77sSuBG59zv5xmbiIjPmAGVK8PIkRwb+hCz7vqCyH9P4N7EcWTMDWJ+hffZcdNwEqteQHr6Nsw8XxZNnz6dNm3a0LRpU/92QETET85YwcrMygIv4kkcSwFfA/c75/YWTngFSxWsRCSQHDsGC2I2k/TSG9y4ezJV+J11XEzIfcO4cGx/joSEUK1aNYYMGXLeo74AACAASURBVMLLL78MwN69e6lSpYqfIxcROX8FVcFqDJ6FVfOAWcB1eEY1RUTkPJUpA90fOrkga0qnaaSElufCf94PkZGEjxzJnL9+xV13PQrAqlWrqFGjBnPnzvVz5CIihedsyWoPYKBzbrBz7n6gC3CLdycAEREpIJe2K8PdiwfQPGkZrFgBt92GmzaNLo+3Z3/LXrzecTZbN1Rm5MhHuOqqqwBYsGABgwYNYt++fX6OXkTEd86WrNYBvjvxxDm3HEgDavkyKBGRkiokBLj8cpgyha3/2cGUJi9Sy+1kxH/60KZXGxpML8VXU49x9Chs2rSJxYsXU768Z1fAJUuWaP9WESl2zjZnNR2o4ZxLzNJ2GGjpnNtcCPEVKM1ZFZGiaPNvGSwa9QW1P5nAtanzySCIBaVupvOc4UR060xQsOfLrtatWxMSEsKyZcsASElJITQ01J+hi4jkKq9zVs+WrGYAXwEpWZpvBBYDR080OOduOvdQC4+SVREpyo4dg/mvbyLplX9x897JXJD6OzRpghs6jMVR/Wl4aRJ79yZw6aWXcvz4cerVq8fIkSN56KGH/B26iMhpCmqB1XRgF55tpk483gW2Z2sTEREfK1MGeoxsQP9dLxC8cztMmwblymH338flN9dicdOx/Dg9hMREOHr0KLfffjuXXnopAPHx8QwbNozNm4vcl2IiUsKdcWS1uNHIqogUR0te+57dT06gS9IswjjGd9aRVe2H0+a57lzRoTRmMG/ePHr16sUPP/xAkyZN2LZtG+np6dSvX9/f4YtICVVQI6siEiASEmawdGk9YmODWLq0HgkJM/wdkgSI9g9cTo+DU1kyaweTLz65IKtupyhmNHwKt2MnXbp0ITExkSZNmgDw4osv0qxZM44cOQKgilkiErCUrIoUAQkJM/j118GkpGwFHCkpW/n118FKWCVTUBBc27syA9eNJGjjBqb0nM/qUq358+ZnsXpR0LMnQbHL2LjBk5SOHDmSd999l/DwcAB69uzJgw8+6MceiIjkTMmqSBGwadMTZGQcPaUtI+MomzY94aeIJJDVbxjE3e/fSKdDn3H4x43w8MMQG0tY12s43rgZE5q8ztr/VeTmm3sAnlHVOnXqUKNGjcznzz77LOvWrfNnN0REAM1ZFSkSYmODgJx+Vo3o6IzCDkeKouRk5t4xh1ofT+Byt4Ikwvl3RD+ODRzOzU80p2rVk6f+9ttvNG3alJiYGAYOHMixY8fYu3cvtWvX9l/8IlLsaM6qSDESGlo3X+0ipwkL46YPB1A/cTnv3r+czyN6cWvSNAa91oL11TvxUZ/ZcPw4AA0bNiQ+Pp7bb78dgH//+9/UrVuXH3/80Z89EJESSsmqSBHQoMHfCAoqe0pbUFBZGjT4m58ikqKqcmW447U2py3I6jG7D0RFwVNPsW/1TkJDK1K2rOe/uXbt2vH888/TqlUrAF599VUGDBhAWlqaP7siIiWEklWRIqB69b5cdNGbhIZGAUZoaBQXXfQm1av39XdoUkRlX5B1/ON50Lo1PPssF1wSxdcVehLTaxEbNziioqJ47LHHCAry/C8jKSmJ/fv3ExISAsAHH3zA2rVr/dkdESnGNGdVREQyud82MecPb3DttslUZh9raUJsk2HUf7o/1/csj7eya6a0tDRq1KhBt27dmDp1KgAHDhygQoUKfoheRIoSzVkVEZF8s4YN6L11PFu+28GkDtM4YhEMW3cfHfvU4r2KQ1nyr19OOT8kJIS1a9cyZswYALZu3Uq1atWYMUPbqolIwVCyKiIip2ndIYxB3w2gQeJy3rnPsyCr5+GptB/SAjp3htmzOZjoWZBVrVo16tb1LPYrXbo0Dz/8MFdddRUAS5cuZdCgQcTHx/utLyJStBVqsmpmlczsYzM7YmZbzezPuZw32sxSzSwpy6NBluOtzOwHMzvq/bNV4fVCJPCp2pUUlMqVod8/PAuyVn62E8aPh+3boU8fUiOjeKvW08x+ZSfJyZ7za9asyfPPP59ZxnX9+vXMmzePcuXKAfDjjz8SFxfnr+6ISBFUqHNWzew9PAnyQKAVMA+4yjm3Jtt5o4FGzrk7crhGaWAD8CoQA9wLPAxc6Jw7fqb7a86qlAQnql1lLSIQFFRWC7Kk4GRksP+9z1l+VwzXpc4ngyDml7qFnbcM5/rnomnYyE45PS0tLXMx1rXXXsvWrVuJi4vDzEhNTaVUqVL+6IWI+FnAzVk1s3DgVuCvzrkk59x/gLlAv3xeKhoIAV51zqU45/4BGPCHgoxXpKhStSvxuaAgKvb9E50OfsbH4zfyXo2HaJ+6iKHv/4GUC5sR0/R1tvx8KPP0E4kqwNtvv83bb7+NmZGRkUHz5s0ZO3asP3ohIkVEYU4DaAykOeeyfv+zCmiWy/ndzGyfma0xs6FZ2psBP7tTh4R/zu06ZjbYzL43s+8TExPPJ36RIiElZVu+2kXOVVgY3PpIA/rtHs/mb3fwVvuTC7KirqoFQ4fCL7+cqDUAQK1atWjXrh0AycnJdO3alebNmwNw+PBh7rvvPjZs2OCP7ohIgCrMZDUCOJSt7SBQLodz5wBNgKrAPcBTZnZ7lusczON1cM696Zy73Dl3edWs9QRFiilVuxJ/uLxjGPf8x7Mga9k/l2O9esHUqdCiBT+W68yETrNZseTUmVrh4eG8/PLLdO/eHfDMZ500aRInBhYSEhLYsmVLYXdFRAJMYSarSUD5bG3lgcPZT3TOrXXO7XLOpTvn/gu8BvTM73VESiJVuxJ/qlwZ2o5o40lUd+4kbtB4qh3fzvDv+lC7g2dB1py/n1yQlVXnzp1JSEjIHHmNiYmhUaNG7N27F4CStC+4iJxUmMlqHBBiZhdmabsEWJPL+Vk5PPNS8Z7f0syyzuBvmcfriBR7qnYlAaNyZRq/9Qhs2MiUW+exutRlDNw9lh4PRfHVBT15o/ciUo6dmoCWL1+eE7/eBw4cyLRp06hSpQoAgwcPZsiQIYXeDRHxr0JLVp1zR4CPgGfMLNzM2gM3A+9kP9fMbjaziuZxBXA/8In3cCyQDtxvZqFmNsLbvtDnnRApIqpX70u7dluIjs6gXbstSlTFrxo0CuLuD/5Ex4Pz+OiFjcys7lmQNWTOHyh9WTN4/XU4dIjsA6d169bljjtObgpTqVIlKlasmPn873//O2vWaJxCpLgr7K2rKgFTgOuA34FRzrmZZtYRWOCci/Ce9x5wPRAK7ABivKv+T1znUmAS0BRYBwx0zv10tvtr6yoRkcDw/XfJRMybzcULY2DFCjLCI5gZ3I/jA4dx8xPNqVz5zK/fs2cPderUYezYsTz66KOkp6cTHx9PZGRk4XRARM5bXreuKtRk1d+UrIqIBKAVK1h5zwQuXjWLMqTwnXXi507Daft8dy5vl/serPv27SMoKIgKFSrw5ZdfcsMNN7Bw4UKio6MLL3YROWcBt8+qiIhIjtq0ocUP0/h25k4mXzyeSLed4Yt7E3lVXd6q9TQfv74zx5dVqlSJChUqANCkSRNGjx7NlVdeCcD06dPp378/R48ezfG1IlJ0KFkVyQNflS9dufJaYmMt87Fy5bUFEoMvy62qlKv4QnAwXH97ZQauewT36wYm9zi5IOum+6OgVy9YtIjTJrZ61alTh6eeeooyZcoAkJiYyKZNmwgLCwNgwYIFmt8qUkRpGoDIWfiqfOnKlddy4MA3p7VXqHANrVp9fc4x+LLcqkq5SmFKToZ5/9xEp7VvUO3TybBvH4dqN2FG+WE0HNOfa3uUJ+gMQy7OOcwM5xz169enZcuWzJ07F4CkpCQiIiIKqScikhPNWc2BklU5F0uX1iMlZetp7aGhUbRrt+Wcrxsba7kei44+9ecyPzH4Kl5fX1vkjJKTYfZs4h6YQOND33OYCD4p14/Ue4Zx0//lbUHWgQMHaNy4Mfv376d27dq88sor3HvvvYUTv4icRnNWRQpIIJQvzU8Mvow3EN4LKaHCwuDOO6n02wreHrGcL8JvpefhKdz1SgvWVu1MTPQcVq5IzfXl1apVo3HjxgCkp6dz//33ZxYfWLt2LYMGDWL79u2F0hURyR8lqyJnEQjlS/MTgy/jDYT3Qkq2KlWg/z/b0P3gNBbP2MnkizwLsoYt7k2ja+rC00/DzpwXZJ28RhWef/55WrZsCcDq1av58MMPCQ0NBWDdunVs2LDB530RkbxRsipyFr4qX1qhwjV5bs9PDL4st6pSrhIogoPhj3+uzMD1ngVZU3p8RukrL4OxYyEqirhLevGvPovYvOnsU9169+5NQkIC1apVA+Cpp56iQ4cOpKenA2T+KSL+oWRV5Cx8Vb60VauvT0tMc1pcld8YfFluVaVcJRA1bBzM3R92ofSX82DDBtIfeJAqqxdy7+w/cLRhc2KaTeDLDw6RkZH7NUqXLp3591dffZWZM2cSHBwMQMeOHXn88cd93Q0RyYUWWImISLHiHKz4NpmV/zeby5ZO4HLnXZBVvh+pgzwVsipVytu1UlNTGTVqFC1atODOO+/MfD5o0CCaNGni246IFHPaDSAHSlZFREqWxESYP2YF4dMm0PWIp0LW/hadqPjkcOjeHUrlXiErJz/88AMdOnRgzpw5dOvWjQMHDnDgwAHq1avnmw6IFGPaDUBEREq8qlVhwOveBVnv7uD9NuOpkLQdeveGqCi+6Tia91/dybFjebte69atiY+P54YbbgA8lbLq16/P5s2bfdgLkZJNI6siIlKypKfD55+T/HIMoYsWkEEQ80p3Z3f34fzxuc7Ub5D7HsjZbdu2jQULFmTu1zpq1Cji4+OZOnUqZnm/jkhJpJFVkQIUFzeM2NgQb1nUEOLihuV6rq9KqOaHSqKKnEFwMHTpAvPm8eHzG5hZ7UE6HF/IkNlXc7RhcyY2n8BXH555QdYJdevWPaWwQOnSpSlTpkxmovrWW2+xevVqX/VEpETQyKrIWcTFDWPXromntdeqNZTGjWNOafNVCdX8UElUkfxxDpYvTmbVE6cuyArq34/wR4dDs2bndN2kpCSqV6/Offfdx7hx43DOER8fT82aNQu4ByJFk0ZWRQrIrl1v5rk9p0Q1t/ZNm544JaEEyMg4yqZNT5xDlL6/rkhxZQZto8MYvOROohJWMG3YctY1uZXw2VOgeXNcdDTvdJvDD//LvUJWTiIiIti2bRsPP/ww4FmcFRkZyaeffuqLbogUW0pWRc4qtw3Bz2+jcF+VLlVJVJFzV7Uq3DmhDVesnQY7dsD48SSv30a/z3pTs10Ub0Xmb0FW5cqVqVq1KgA1a9bkySefpEOHDgDMnTuXAQMGsH//fh/1RqR4ULIqclbB+WzPG1+VLlVJVJECUqUKPPIIe5ZsYNItn7EmpBUDdz1D9wej+OKCXvzr9tg8Vcg6ITIykmeeeYaKFSsCsGPHDr7//nvKly8PQGxsLGvWrPFJV0SKskJNVs2skpl9bGZHzGyrmf05l/MeMbNfzOywmW02s0eyHd9iZslmluR9fFk4PZCSqFatwXlu91UJ1fxQSVSRglWvYTCDPu5C+4Pz+eC5kwuy7p11NakXNyfj9Qlw6FC+rzts2DB++eWXzEpZDzzwwCmLtZKTkwusDyJFWWGPrE4AjgPVgb7ARDPLaea6Af2BisANwAgz65PtnG7OuQjv43pfBi0lW+PGMdSqNZSTI6nBOS6uAt+VUM0PlUQV8Y2yZeG2xxvSL/5FNi7awZvtplKuelmC7hsBkZEk3z2cqSPXsG9f3q+ZdXurr776iokTPYs5jx07Rt26dXnllVcKuhsiRU6h7QZgZuHAfqC5cy7O2/YOsNM5N+osr/0Hnljv8z7fAgxyzp2eAZyBdgMQEZECt3w5xMSQNmMWIWkpfGudWdN5GFeM607rtvmrkHXCgQMHGDduHF26dKFjx47s2LGDMWPG8Pjjj9OgQYMC7oCIfwTibgCNgbQTiarXKuCMe4KY55+dHYHsE3lmmFmimX1pZpcUbKgiIiJ5dMUVMG0a/529g0mNX6CO28rQ2N7UvNK7IOu1XXlekHVChQoVGDduHB07dgRg5cqVzJo1iwzv5q+bN29mw4YNBd0TkYBUmMlqBJB9Us9BoNxZXjcaT5xTs7T1BeoBUcAi4Aszq5DTi81ssJl9b2bfJyYmnkPYIiIiZ9epRxUG/fooaes28tbNWRZk/aUuP13YC2JjPZu6noOuXbuSmJhIo0aNABg3bhyXXnpp5rzWjLxUMBApogpzGsClwBLnXNksbQ8D0c65brm8ZgTwMNDRObfjDNdeDzzinDvj5nWaBiAiIoXl6FH47LXfOPbqG/RJnkLpw/ugaVN23jKMtZf145ru5Qk6xyGjnTt38tNPP9G1a1fAk8w2atSIV199tQB7IOJbeZ0GEFIYwXjFASFmdqFz7sR3F5dw+tf7AJjZ3cAooNOZElUvh2dRlhQzCQkz2LTpCVJSthEaWpcGDf5WYAuFPJWp3sSzX2owtWoNznHRFMCyZc1ITl6b+TwsrClt2+a8xUxsbGkg6+bhpYiOPp7jud9+W5GMjAOZz4OCKtCpU857Li5ZEklq6q6TVy1Vi/btd+Z4ri/fN19eW6Q4ObEgy416EZKfgdmzYMIEIp8bQXlGMfOC/qQPHsZNjzfDu5tVnkVGRhIZGQmAc46LL76Y2rVrZz5/6qmn6N27N82bNy/obokUukItt2pms/AkloOAVsB84Crn3Jps5/UFXgauds6ty3asLlAHWIFnesB9wKPAxc653890f42sFi2+LBuanxKq2RPVE3JKWE9PVE84PWHNnqiekFPCmj1RzbxqDgmrL983lXIVOX8zHlhO6ckxdDsyizKk8G1QZ9ZED6ftc7dw2TkuyMoqLi6OSy65hDfeeIMBAwZw9OhR9uzZQ7169c4/eJECFIgLrACGAWHAHuA9YKhzbo2ZdTSzpCznPQtUBlZk2Uv1De+xcsBEPDsL7MSztdWNZ0tUpejxZdnQ/JRQzSlRzb09t3KMp7fnlKjm1p5Toppbuy/fN5VyFTl/fV+7gh4HpxH7zg4mXfgCdTK2MnThbdS4MoplN46GXTn/vOdV48aNiY+P57bbbgPgo48+on79+qxataoAohcpfIWarDrn9jnnbnHOhTvn6jrnZnrbv3PORWQ5r75zrlSWfVQjnHNDvMfWOOdaeq9R2Tl3jXNOw6XFkG/LhvqmhGog8OX7plKuIgUjOBhuuKMKg+IeJXWtd0FWqVZc8cUzULcu9OrFmgmxbN1ybt9+XnDBBYSFhQHQuXNnXnnlFVq2bAnACy+8QN++fUlPL/q/76RkULlVCVi+LRvqmxKqgcCX75tKuYoUvMZNgrnn313onDQf27AB/vIX3Dff0GzE1Ryu34KJLWP4+uPDnOuC/zp16vDggw9mFiBIS0sjNTU1s3LWe++9x88//1xQ3REpcEpWJWD5smxofkqohoU1zfHcnNtzm292entQUI67reXYXqpUrZyvmkO7L983lXIV8Z3SpYGGDeGll0hav5M3r5xCipVh6OrhtO1Ri5mVhvP2Y2vYn/MazDx74oknmDNnDgCpqamMGDGC119/PfP43r17z+8GIgVMyaoELF+WDc1PCdW2bdeclpjmthuAZxFV9sQ0590AOnXaf1pimttuAO3b7zwtMc1tNwBfvm8q5SpSOMpVC2Pw0ruoE/8904Yu44vwHvQ8OJn+45uzuko0G557H1JzmyOfd6VKlSIuLo6nn34agN9++40aNWowc+bM8762SEEp1N0A/E27AYiISFGUng5fztzLzrFTuH7TROqmb4GaNWHwYH5oPZjm19ciNPT87xMfH09MTAz33HMPderUYfHixUyZMoXx48dTvXr187+BSBZ53Q1AyaqIiEgRcnBfOhf8dwHExOA+/5w0F8z80t1JuHUYf3yuM1H1Cm7b8alTpzJ69GjWr19PWFgYy5cvJyIigqZNc54eJZIfSlZzoGRVRESKk7gFv/G/ARPpmjiFSuznF5rxnxbDuPCZflx9U7lzrpCVVUZGBkHeC0VHR7Nnzx7WrFmDmZGSkkJoQQzpSokUqPusioiISAFpfGND+iW8xIZFJxdkDVk9nCu61+K9SsNJWpZzpb38CMqS8c6ZM4fp06djZqSnp3PxxRczduzY876HyJkoWZViIyFhBkuX1iM2NoilS+uRkDAj4ONYtqwZsbGW+Vi2rFkhRioixYEZtI32LsjavYKpQ5bxRdke9Do0mYgrm8PVV8P777Pu5/NfkFWtWjXatGkDwLFjx+jduzeXXXYZAAcOHGDIkCHExcWd931EstI0ACkWAqUMaH7iyE8ZVxGR/EhLg73r91Jj/hSYOBG2bGEXNVlQezAVHh1M18EFsyArq9jYWLp27cqiRYto06YNu3fv5siRIzRq1KhgbyTFhqYBSIkSKGVA8xNH/sq4iojkXUgI1GheBR59FDZuZPHIT/klpBUDd4zhpvuj+PyC23jrjsVs21pwA1bR0dEkJCRw+eWe3OOf//wnTZo0Yd++fQCUpMExKVhKVqVYCJQyoIESh4hIpuBgOr/YlfYH5jPr2Y3MrPoAHVO+5p4Z0Ryq14K3r4yBw4cL5Fbh4eGZlbKGDx/OzJkzqVSpEgADBgxg8OCcC7KInImSVSkWAqUMaKDEISKSXXg49HmiIf0TXiLumx38q61nQVb/ZcOhVi0YPpyUH8+/QtYJkZGR9OrVK/N57dq1qVXrZHGTcePGqcyr5ImSVSkWAqUMaH7iyF8ZVxGRgmEGV/6hLPf+7y5q71rB7/OXQY8eMHkyoa2b83OVq5l4zfv8tPz8F2Rl9dxzzzF69GgAdu/ezZgxY/jmm28ASEtLY+vWrQV6Pyk+lKxKsRAoZUDzE0d+yriKiPhC9RpG5RuvgOnTYccO5l41jqiMzQxdeBvV2tZjUp0xfPj6blJSCva+NWvWJD4+noEDBwLw5ZdfUq9ePb799tuCvZEUC9oNQERERDL9ujadxaMWUH/+BK5L/5xUQpgf2h0bPpybXurkGZotYDt37uSdd97hoYceonTp0rz11lssXLiQKVOmEBYWVuD3k8Cg3QBEREQk3y5qGszguV256uAC3nt2IzOreBZk3fRKNLRoATExHIk/TEZGwd0zMjKSUaNGUbp0aQAOHTrEnj17MhPVTz75hFWrVhXcDaVI0ciqiIiI5Mo5WPrNUVpvnE3opAnwww8cK1WOD8r2J2PIMG4a1ZQKFXx5f0dUVBRt2rThww8/BDwFCCr48qZSKDSyKiIiIufNDK66tiyhQ+6CFSvI+O//+KJsd3oenET/F5qxqvLVTLz2A1auKNgFWSfvb/z000+8+OKLACQmJlKjRg3efPNNn9xPAk+hJqtmVsnMPjazI2a21cz+nMt5ZmYvmNnv3scLZicnyZhZKzP7wcyOev9sVXi9EBERKaHMCGrXli57p/PNtB281dC7IOubXlS9oh6T6o7h+093F/htK1euTIMGDbwhGI8++igdOnQAYNWqVQwYMIDt27cX+H0lMBT2yOoE4DhQHegLTDSznIqhDwZuAS4BWgLdgHsBzKw08AnwLlARmA584m0XERERHwsJgS4DqnDPxsc49stvvNntU9YGt2TQ9tG07l4XbrsNFi8mLbXgpxpWqVKFZ555hqZNPbuprF+/ngULFhAeHg7A6tWrWbtWlQCLk0Kbs2pm4cB+oLlzLs7b9g6w0zk3Ktu5/wWmOefe9D4fCNzjnLvSzK4HpgK1nTd4M9sGDHbOfX6mGDRnVURExDeSkuDbqRv509Y3YMoU2L+fzeHN+LLhMC58ph/R3coR5KMhsrS0NEJCQgDo3r07y5YtY/v27QQHB59yTAJLXuesFuan1xhIO5Goeq0COudwbjPvsaznNcty7Gd3apb9s7f9tGTVzAbjGakFSDGzX84tfPGzKsBefwch50yfX9Gmz6/o8u9nd2QN/Dwcbhle6LcuJglqcf/Zi8rLSYX5SUYAh7K1HQTK5XLuwWznRXjnrWY/dqbr4B2dPTFC+31eMngJPPrsijZ9fkWbPr+iS59d0abPz6Mw56wmAeWztZUHDufh3PJAknc0NT/XEREREZEirDCT1TggxMwuzNJ2CZBTbck13mM5nbcGaJl1dwA8i7BUo1JERESkmCm0ZNU5dwT4CHjGzMLNrD1wM/BODqe/DTxkZpFmVgt4GJjmPRYLpAP3m1momY3wti/MQxjalK3o0mdXtOnzK9r0+RVd+uyKNn1+FHIFKzOrBEwBrgN+B0Y552aaWUdggXMuwnueAS8Ag7wvnQQ8lmX1/6XetqbAOmCgc+6nQuuIiIiIiBSKElVuVURERESKFpVbFREREZGApWRVRERERAJWiUhWzaySmX1sZkfMbKuZ/dnfMUnemNkIM/vezFLMbJq/45G88y6AnOz9mTtsZivN7EZ/xyV5Z2bvmtluMztkZnFmNujsr5JAYmYXmtkxM3vX37FI3plZrPdzS/I+fvV3TP5UIpJVYAJwHKgO9AUmmlmzM79EAsQu4Fk8C/OkaAkBtuOpUncB8CQwx8zq+TEmyZ/ngXrOufLATcCzZtbazzFJ/kwAVvg7CDknI5xzEd7HRf4Oxp+KfbJqZuHArcBfnXNJzrn/AHOBfv6NTPLCOfeRc+7feHaPkCLEOXfEOTfaObfFOZfhnPsM2Awo2SkinHNrnHMpJ556Hw39GJLkg5n1AQ4A3/g7FpHzUeyTVaAxkOaci8vStgrQyKpIITKz6nh+HlXAowgxsxgzOwqsB3YD8/0ckuSBmZUHngEe8ncscs6eN7O9ZrbEzKL9HYw/lYRkNQI4lK3tIFDOD7GIlEhmVgqYAUx3zq33dzySd865YXh+X3bEU9gl5cyvkAAxFpjsnNvh70DknDwGNAAi8RQG+NTMSuy3GiUhWU0CymdrKw8c9kMsIiWOmQXhqVR3HBhxltMlGQ2i7QAABZVJREFUADnn0r1TqGoDQ/0dj5yZmbUCrgX+7u9Y5Nw455Y55w4751Kcc9OBJcCf/B2Xv4T4O4BCEAeEmNmFzrkN3rZL0FeRIj7nrUY3Gc/ixj8551L9HJKcnxA0Z7UoiAbqAds8P4JEAMFm1tQ5d5kf45Jz5wDzdxD+UuxHVp1zR/B8dfWMmYWbWXvgZjwjPRLgzCzEzMoAwXh+2ZYxs5Lwj6ziYiLQBOjmnEv2dzCSd2ZWzcz6mFmEmQWb2R+B29FinaLgTTz/qGjlfbwBzAP+6M+gJG/MrIKZ/fHE/+/MrC/QCfjc37H5S7FPVr2GAWHAHuA9YKhzTiOrRcOTQDIwCrjD+/cn/RqR5ImZRQH34vmfZXyW/QL7+jk0yRuH5yv/HcB+4CXgL865uX6NSs7KOXfUORd/4oFnOtwx51yiv2OTPCmFZ8vGRGAvcB9wS7aF4iWKOef8HYOIiIiISI5KysiqiIiIiBRBSlZFREREJGApWRURERGRgKVkVUREREQClpJVEREREQlYSlZFREREJGApWRURCQBmdqeZJZ3lnC1mNrKwYjoTM6tnZs7MLvd3LCJSvClZFRHxMrNp3gTMmVmqmW0ys5fMLDyf1/jMl3EWtuLYJxEpOlS2UkTkVF8D/fBUkekITALC8VRzEhGRQqaRVRGRU6V4y1Rud87NBGYAt5w4aGZNzWyemR02sz1m9p6Z1fAeGw0MALpkGaGN9h4bZ2a/mlmy9+v88WZW5nwCNbMLzOxNbxyHzWxx1q/lT0wtMLNrzOwXMztiZovMrH626zxuZgnec982s6fNbMvZ+uQVZWZfmdlRM1trZtedT59ERLJTsioicmbJeEZZMbOawLfAL8AVwLVABPCJmQUBLwFz8IzO1vQ+/uu9zhHgbqAJMAzoAzxxrkGZmQHzgEigK3CpN7aF3jhPCAUe9967HVABeCPLdfoAT3tjuQxYBzyU5fVn6hPA34B/AJcAK4BZZhZxrv0SEclO0wBERHJhZlcAfwa+8TYNBVY55x7Lck5/YB9wuXNuuZkl4x2dzXot59zYLE+3mNlzwEjgr+cY3tVAK6Cqcy7Z2/ZXM+uGZxrDeG9bCDDcOferN96XgClmZs45BzwATHPOTfKe/7yZXQ009sadlFOfPLkyAH93zn3qbfs/oL83rv+cY79ERE6hZFVE5FQ3eFflh+AZUf0EuM97rDXQKZdV+w2B5bld1Mx6An8BGuEZjQ32Ps5Va6AskJglcQQo443lhJQTiarXLqA0UBFPkn0x8Fa2ay/Dm6zmwc/Zrg1QLY+vFRE5KyWrIiKn+hYYDKQCu5xzqVmOBeH56j2n7aMScrugmV0JzALGAA8CB4Cb8HzFfq6CvPfsmMOxQ1n+npbtmMvy+oKQ+f4455w3cdYUMxEpMEpWRUROddQ5tzGXYz8CtwFbsyWxWR3n9BHT9sDOrFMBzCzqPOP8EagOZDjnNp3HddYDbYApWdquyHZOTn0SESkU+teviEjeTQAuAGabWVsza2Bm13pX5JfznrMFaG5mF5lZFTMrBcQBkWbW1/uaocDt5xnL18ASPIu7bjSz+mbWzszGmFlOo625eQ2408zuNrMLzexRoC0nR2Bz65OISKFQsioikkfOuV14RkkzgM+BNXgS2BTvAzzzP9cB3wOJQHvvAqQXgVfxzPG8DnjqPGNxwJ+Ahd57/opn1f5FnJw7mpfrzALGAuOAn4DmeHYLOJbltNP6dD6xi4jkh3l+34mIiHiY2cdAiHOum79jERHRnFURkRLMzMri2ZLrczyLsW4Fbvb+KSLidxpZFREpwcwsDPgUT1GBMGAD8IK3epeIiN8pWRURERGRgKUFViIiIiISsJSsioiIiEjAUrIqIiIiIgFLyaqIiIiIBCwlqyIiIiISsP4fvFxwFwIbFgEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# Transform the decision boundary lines back to the original scale\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# Plot all three decision boundaries\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "\n",
    "mnist = fetch_mldata(\"MNIST original\")\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"]\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "rnd_idx = np.random.permutation(60000)\n",
    "X_train = X_train[rnd_idx]\n",
    "y_train = y_train[rnd_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8263333333333334"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, 82% accuracy on MNIST is a really bad performance. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9224"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's much better (we cut the error rate in two), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default).\n",
    "\n",
    "**Warning**: if you are using Scikit-Learn ≤ 0.19, the `SVC` class will use the One-vs-One (OvO) strategy by default, so you must explicitly set `decision_function_shape=\"ovr\"` if you want to use the OvR strategy instead (OvR is the default since 0.19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(decision_function_shape=\"ovr\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94615"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.001766074650481071, C=8.852316058423087 .................\n",
      "[CV] .. gamma=0.001766074650481071, C=8.852316058423087, total=   0.6s\n",
      "[CV] gamma=0.001766074650481071, C=8.852316058423087 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. gamma=0.001766074650481071, C=8.852316058423087, total=   0.6s\n",
      "[CV] gamma=0.001766074650481071, C=8.852316058423087 .................\n",
      "[CV] .. gamma=0.001766074650481071, C=8.852316058423087, total=   0.6s\n",
      "[CV] gamma=0.006364737055453384, C=1.8271960104746645 ................\n",
      "[CV] . gamma=0.006364737055453384, C=1.8271960104746645, total=   0.7s\n",
      "[CV] gamma=0.006364737055453384, C=1.8271960104746645 ................\n",
      "[CV] . gamma=0.006364737055453384, C=1.8271960104746645, total=   0.7s\n",
      "[CV] gamma=0.006364737055453384, C=1.8271960104746645 ................\n",
      "[CV] . gamma=0.006364737055453384, C=1.8271960104746645, total=   0.7s\n",
      "[CV] gamma=0.051349833451870636, C=9.875199193765326 .................\n",
      "[CV] .. gamma=0.051349833451870636, C=9.875199193765326, total=   0.7s\n",
      "[CV] gamma=0.051349833451870636, C=9.875199193765326 .................\n",
      "[CV] .. gamma=0.051349833451870636, C=9.875199193765326, total=   0.7s\n",
      "[CV] gamma=0.051349833451870636, C=9.875199193765326 .................\n",
      "[CV] .. gamma=0.051349833451870636, C=9.875199193765326, total=   0.7s\n",
      "[CV] gamma=0.05991666578466177, C=6.59992909281409 ...................\n",
      "[CV] .... gamma=0.05991666578466177, C=6.59992909281409, total=   0.7s\n",
      "[CV] gamma=0.05991666578466177, C=6.59992909281409 ...................\n",
      "[CV] .... gamma=0.05991666578466177, C=6.59992909281409, total=   0.7s\n",
      "[CV] gamma=0.05991666578466177, C=6.59992909281409 ...................\n",
      "[CV] .... gamma=0.05991666578466177, C=6.59992909281409, total=   0.7s\n",
      "[CV] gamma=0.003596490522533181, C=9.053435975487119 .................\n",
      "[CV] .. gamma=0.003596490522533181, C=9.053435975487119, total=   0.6s\n",
      "[CV] gamma=0.003596490522533181, C=9.053435975487119 .................\n",
      "[CV] .. gamma=0.003596490522533181, C=9.053435975487119, total=   0.7s\n",
      "[CV] gamma=0.003596490522533181, C=9.053435975487119 .................\n",
      "[CV] .. gamma=0.003596490522533181, C=9.053435975487119, total=   0.7s\n",
      "[CV] gamma=0.004002330992905356, C=2.701062804458301 .................\n",
      "[CV] .. gamma=0.004002330992905356, C=2.701062804458301, total=   0.6s\n",
      "[CV] gamma=0.004002330992905356, C=2.701062804458301 .................\n",
      "[CV] .. gamma=0.004002330992905356, C=2.701062804458301, total=   0.7s\n",
      "[CV] gamma=0.004002330992905356, C=2.701062804458301 .................\n",
      "[CV] .. gamma=0.004002330992905356, C=2.701062804458301, total=   0.7s\n",
      "[CV] gamma=0.017596957507461645, C=3.2711787843881437 ................\n",
      "[CV] . gamma=0.017596957507461645, C=3.2711787843881437, total=   0.7s\n",
      "[CV] gamma=0.017596957507461645, C=3.2711787843881437 ................\n",
      "[CV] . gamma=0.017596957507461645, C=3.2711787843881437, total=   0.7s\n",
      "[CV] gamma=0.017596957507461645, C=3.2711787843881437 ................\n",
      "[CV] . gamma=0.017596957507461645, C=3.2711787843881437, total=   0.7s\n",
      "[CV] gamma=0.01573529056426603, C=6.848991127746501 ..................\n",
      "[CV] ... gamma=0.01573529056426603, C=6.848991127746501, total=   0.7s\n",
      "[CV] gamma=0.01573529056426603, C=6.848991127746501 ..................\n",
      "[CV] ... gamma=0.01573529056426603, C=6.848991127746501, total=   0.7s\n",
      "[CV] gamma=0.01573529056426603, C=6.848991127746501 ..................\n",
      "[CV] ... gamma=0.01573529056426603, C=6.848991127746501, total=   0.7s\n",
      "[CV] gamma=0.03834647526105027, C=2.893035364914488 ..................\n",
      "[CV] ... gamma=0.03834647526105027, C=2.893035364914488, total=   0.7s\n",
      "[CV] gamma=0.03834647526105027, C=2.893035364914488 ..................\n",
      "[CV] ... gamma=0.03834647526105027, C=2.893035364914488, total=   0.7s\n",
      "[CV] gamma=0.03834647526105027, C=2.893035364914488 ..................\n",
      "[CV] ... gamma=0.03834647526105027, C=2.893035364914488, total=   0.7s\n",
      "[CV] gamma=0.008808538172595842, C=5.336260835426313 .................\n",
      "[CV] .. gamma=0.008808538172595842, C=5.336260835426313, total=   0.7s\n",
      "[CV] gamma=0.008808538172595842, C=5.336260835426313 .................\n",
      "[CV] .. gamma=0.008808538172595842, C=5.336260835426313, total=   0.7s\n",
      "[CV] gamma=0.008808538172595842, C=5.336260835426313 .................\n",
      "[CV] .. gamma=0.008808538172595842, C=5.336260835426313, total=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   29.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f3829037908>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f3829037358>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.856"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set (run this at night, it will take hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99965"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9709"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.949968822217229"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9746634404845752"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.07969454818643928, C=4.745401188473625 ..................\n",
      "[CV] ... gamma=0.07969454818643928, C=4.745401188473625, total=   6.9s\n",
      "[CV] gamma=0.07969454818643928, C=4.745401188473625 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    9.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ... gamma=0.07969454818643928, C=4.745401188473625, total=   6.9s\n",
      "[CV] gamma=0.07969454818643928, C=4.745401188473625 ..................\n",
      "[CV] ... gamma=0.07969454818643928, C=4.745401188473625, total=   7.0s\n",
      "[CV] gamma=0.015751320499779724, C=8.31993941811405 ..................\n",
      "[CV] ... gamma=0.015751320499779724, C=8.31993941811405, total=   6.6s\n",
      "[CV] gamma=0.015751320499779724, C=8.31993941811405 ..................\n",
      "[CV] ... gamma=0.015751320499779724, C=8.31993941811405, total=   6.4s\n",
      "[CV] gamma=0.015751320499779724, C=8.31993941811405 ..................\n",
      "[CV] ... gamma=0.015751320499779724, C=8.31993941811405, total=   6.5s\n",
      "[CV] gamma=0.002051110418843397, C=2.560186404424365 .................\n",
      "[CV] .. gamma=0.002051110418843397, C=2.560186404424365, total=   6.0s\n",
      "[CV] gamma=0.002051110418843397, C=2.560186404424365 .................\n",
      "[CV] .. gamma=0.002051110418843397, C=2.560186404424365, total=   6.0s\n",
      "[CV] gamma=0.002051110418843397, C=2.560186404424365 .................\n",
      "[CV] .. gamma=0.002051110418843397, C=2.560186404424365, total=   6.1s\n",
      "[CV] gamma=0.05399484409787431, C=1.5808361216819946 .................\n",
      "[CV] .. gamma=0.05399484409787431, C=1.5808361216819946, total=   6.0s\n",
      "[CV] gamma=0.05399484409787431, C=1.5808361216819946 .................\n",
      "[CV] .. gamma=0.05399484409787431, C=1.5808361216819946, total=   6.8s\n",
      "[CV] gamma=0.05399484409787431, C=1.5808361216819946 .................\n",
      "[CV] .. gamma=0.05399484409787431, C=1.5808361216819946, total=   6.0s\n",
      "[CV] gamma=0.026070247583707663, C=7.011150117432088 .................\n",
      "[CV] .. gamma=0.026070247583707663, C=7.011150117432088, total=   6.5s\n",
      "[CV] gamma=0.026070247583707663, C=7.011150117432088 .................\n",
      "[CV] .. gamma=0.026070247583707663, C=7.011150117432088, total=   6.7s\n",
      "[CV] gamma=0.026070247583707663, C=7.011150117432088 .................\n",
      "[CV] .. gamma=0.026070247583707663, C=7.011150117432088, total=   7.3s\n",
      "[CV] gamma=0.0870602087830485, C=1.2058449429580245 ..................\n",
      "[CV] ... gamma=0.0870602087830485, C=1.2058449429580245, total=   6.3s\n",
      "[CV] gamma=0.0870602087830485, C=1.2058449429580245 ..................\n",
      "[CV] ... gamma=0.0870602087830485, C=1.2058449429580245, total=   6.1s\n",
      "[CV] gamma=0.0870602087830485, C=1.2058449429580245 ..................\n",
      "[CV] ... gamma=0.0870602087830485, C=1.2058449429580245, total=   6.6s\n",
      "[CV] gamma=0.0026587543983272693, C=9.324426408004218 ................\n",
      "[CV] . gamma=0.0026587543983272693, C=9.324426408004218, total=   6.5s\n",
      "[CV] gamma=0.0026587543983272693, C=9.324426408004218 ................\n",
      "[CV] . gamma=0.0026587543983272693, C=9.324426408004218, total=   6.2s\n",
      "[CV] gamma=0.0026587543983272693, C=9.324426408004218 ................\n",
      "[CV] . gamma=0.0026587543983272693, C=9.324426408004218, total=   6.5s\n",
      "[CV] gamma=0.0023270677083837795, C=2.818249672071006 ................\n",
      "[CV] . gamma=0.0023270677083837795, C=2.818249672071006, total=   6.0s\n",
      "[CV] gamma=0.0023270677083837795, C=2.818249672071006 ................\n",
      "[CV] . gamma=0.0023270677083837795, C=2.818249672071006, total=   6.1s\n",
      "[CV] gamma=0.0023270677083837795, C=2.818249672071006 ................\n",
      "[CV] . gamma=0.0023270677083837795, C=2.818249672071006, total=   6.1s\n",
      "[CV] gamma=0.011207606211860567, C=4.042422429595377 .................\n",
      "[CV] .. gamma=0.011207606211860567, C=4.042422429595377, total=   6.3s\n",
      "[CV] gamma=0.011207606211860567, C=4.042422429595377 .................\n",
      "[CV] .. gamma=0.011207606211860567, C=4.042422429595377, total=   6.3s\n",
      "[CV] gamma=0.011207606211860567, C=4.042422429595377 .................\n",
      "[CV] .. gamma=0.011207606211860567, C=4.042422429595377, total=   6.7s\n",
      "[CV] gamma=0.003823475224675185, C=5.319450186421157 .................\n",
      "[CV] .. gamma=0.003823475224675185, C=5.319450186421157, total=   6.0s\n",
      "[CV] gamma=0.003823475224675185, C=5.319450186421157 .................\n",
      "[CV] .. gamma=0.003823475224675185, C=5.319450186421157, total=   6.6s\n",
      "[CV] gamma=0.003823475224675185, C=5.319450186421157 .................\n",
      "[CV] .. gamma=0.003823475224675185, C=5.319450186421157, total=   6.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  4.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "          fit_params=None, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f382860fa58>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f382860fc50>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785356"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.592916838552874"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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